Overview

Dataset statistics

Number of variables34
Number of observations19565
Missing cells0
Missing cells (%)0.0%
Duplicate rows268
Duplicate rows (%)1.4%
Total size in memory5.1 MiB
Average record size in memory272.0 B

Variable types

Categorical27
Numeric7

Warnings

tipo_de_excepcionalidade has constant value "----------" Constant
Dataset has 268 (1.4%) duplicate rows Duplicates
cliente has a high cardinality: 3795 distinct values High cardinality
cnpj has a high cardinality: 3788 distinct values High cardinality
descricao_do_projeto has a high cardinality: 5535 distinct values High cardinality
municipio has a high cardinality: 1057 distinct values High cardinality
data_da_contratacao has a high cardinality: 2974 distinct values High cardinality
fonte_de_recurso_desembolsos has a high cardinality: 89 distinct values High cardinality
instrumento_financeiro has a high cardinality: 175 distinct values High cardinality
subsetor_cnae_codigo has a high cardinality: 626 distinct values High cardinality
subsetor_cnae_nome has a high cardinality: 614 distinct values High cardinality
valor_contratado_reais is highly correlated with valor_desembolsado_reaisHigh correlation
valor_desembolsado_reais is highly correlated with valor_contratado_reaisHigh correlation
custo_financeiro is highly correlated with tipo_de_excepcionalidade and 1 other fieldsHigh correlation
uf is highly correlated with tipo_de_excepcionalidadeHigh correlation
subsetor_bndes is highly correlated with tipo_de_excepcionalidade and 3 other fieldsHigh correlation
tipo_de_excepcionalidade is highly correlated with custo_financeiro and 17 other fieldsHigh correlation
modalidade_de_apoio is highly correlated with custo_financeiro and 4 other fieldsHigh correlation
setor_bndes is highly correlated with subsetor_bndes and 2 other fieldsHigh correlation
porte_do_cliente is highly correlated with tipo_de_excepcionalidadeHigh correlation
subsetor_cnae_agrupado is highly correlated with subsetor_bndes and 3 other fieldsHigh correlation
area_operacional is highly correlated with tipo_de_excepcionalidadeHigh correlation
cnpj_da_instituicao_financeira_credenciada is highly correlated with tipo_de_excepcionalidade and 2 other fieldsHigh correlation
produto is highly correlated with tipo_de_excepcionalidade and 1 other fieldsHigh correlation
setor_cnae is highly correlated with subsetor_bndes and 2 other fieldsHigh correlation
inovacao is highly correlated with tipo_de_excepcionalidadeHigh correlation
fonte_de_recurso_desembolsos is highly correlated with tipo_de_excepcionalidade and 1 other fieldsHigh correlation
instituicao_financeira_credenciada is highly correlated with tipo_de_excepcionalidade and 2 other fieldsHigh correlation
tipo_de_garantia is highly correlated with tipo_de_excepcionalidade and 2 other fieldsHigh correlation
forma_de_apoio is highly correlated with tipo_de_excepcionalidade and 3 other fieldsHigh correlation
situacao_do_contrato is highly correlated with tipo_de_excepcionalidadeHigh correlation
natureza_do_cliente is highly correlated with tipo_de_excepcionalidadeHigh correlation
valor_contratado_reais is highly skewed (γ1 = 21.17503098) Skewed
valor_desembolsado_reais is highly skewed (γ1 = 24.74748587) Skewed
municipio_codigo has 5357 (27.4%) zeros Zeros
valor_desembolsado_reais has 1341 (6.9%) zeros Zeros
juros has 2045 (10.5%) zeros Zeros
prazo_carencia_meses has 1850 (9.5%) zeros Zeros
prazo_amortizacao_meses has 1283 (6.6%) zeros Zeros

Reproduction

Analysis started2021-08-19 02:54:55.554049
Analysis finished2021-08-19 02:55:51.721532
Duration56.17 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

cliente
Categorical

HIGH CARDINALITY

Distinct3795
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
SUZANO S.A.
 
198
COMPANHIA PAULISTA DE FORCA E LUZ
 
140
PETROBRAS TRANSPORTE S.A - TRANSPETRO
 
139
STARNAV SERVICOS MARITIMOS LTDA.
 
134
KLABIN S.A.
 
124
Other values (3790)
18830 

Length

Max length55
Median length31
Mean length31.87533861
Min length3

Characters and Unicode

Total characters623641
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1350 ?
Unique (%)6.9%

Sample

1st rowMUNICIPIO DE RIBEIRAO PRETO
2nd rowINSTITUTO DE DESENVOLVIMENTO SUSTENTAVEL DO BAIXO SUL D
3rd rowINSTITUTO DE DESENVOLVIMENTO SUSTENTAVEL DO BAIXO SUL D
4th rowINSTITUTO DE DESENVOLVIMENTO SUSTENTAVEL DO BAIXO SUL D
5th rowACEF S/A
ValueCountFrequency (%)
SUZANO S.A.198
 
1.0%
COMPANHIA PAULISTA DE FORCA E LUZ140
 
0.7%
PETROBRAS TRANSPORTE S.A - TRANSPETRO139
 
0.7%
STARNAV SERVICOS MARITIMOS LTDA.134
 
0.7%
KLABIN S.A.124
 
0.6%
BRENCO - COMPANHIA BRASILEIRA DE ENERGIA RENOVAVEL - EM124
 
0.6%
RIO GRANDE ENERGIA SA122
 
0.6%
CIA DE SANEAMENTO BASICO DO ESTADO DE SAO PAULO SABESP121
 
0.6%
BRASKEM S.A.118
 
0.6%
COMPANHIA PIRATININGA DE FORCA E LUZ116
 
0.6%
Other values (3785)18229
93.2%
2021-08-18T23:55:52.346535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s.a6508
 
6.9%
de6311
 
6.7%
ltda3580
 
3.8%
s/a3260
 
3.4%
do2531
 
2.7%
e2356
 
2.5%
1982
 
2.1%
energia1978
 
2.1%
companhia1505
 
1.6%
brasil1312
 
1.4%
Other values (4328)63545
67.0%

Most occurring characters

ValueCountFrequency (%)
A80887
13.0%
77212
12.4%
E53692
 
8.6%
I47441
 
7.6%
O45645
 
7.3%
S44320
 
7.1%
R39888
 
6.4%
N29930
 
4.8%
T28254
 
4.5%
C26338
 
4.2%
Other values (36)150034
24.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter525370
84.2%
Space Separator77212
 
12.4%
Other Punctuation17287
 
2.8%
Dash Punctuation2347
 
0.4%
Decimal Number1169
 
0.2%
Open Punctuation128
 
< 0.1%
Close Punctuation128
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A80887
15.4%
E53692
10.2%
I47441
9.0%
O45645
8.7%
S44320
8.4%
R39888
 
7.6%
N29930
 
5.7%
T28254
 
5.4%
C26338
 
5.0%
D23976
 
4.6%
Other values (16)104999
20.0%
ValueCountFrequency (%)
0255
21.8%
1177
15.1%
4138
11.8%
7114
9.8%
2108
9.2%
3100
 
8.6%
599
 
8.5%
971
 
6.1%
861
 
5.2%
646
 
3.9%
ValueCountFrequency (%)
.13620
78.8%
/3365
 
19.5%
,206
 
1.2%
&83
 
0.5%
'9
 
0.1%
!4
 
< 0.1%
ValueCountFrequency (%)
77212
100.0%
ValueCountFrequency (%)
-2347
100.0%
ValueCountFrequency (%)
(128
100.0%
ValueCountFrequency (%)
)128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin525370
84.2%
Common98271
 
15.8%

Most frequent character per script

ValueCountFrequency (%)
A80887
15.4%
E53692
10.2%
I47441
9.0%
O45645
8.7%
S44320
8.4%
R39888
 
7.6%
N29930
 
5.7%
T28254
 
5.4%
C26338
 
5.0%
D23976
 
4.6%
Other values (16)104999
20.0%
ValueCountFrequency (%)
77212
78.6%
.13620
 
13.9%
/3365
 
3.4%
-2347
 
2.4%
0255
 
0.3%
,206
 
0.2%
1177
 
0.2%
4138
 
0.1%
(128
 
0.1%
)128
 
0.1%
Other values (10)695
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII623641
100.0%

Most frequent character per block

ValueCountFrequency (%)
A80887
13.0%
77212
12.4%
E53692
 
8.6%
I47441
 
7.6%
O45645
 
7.3%
S44320
 
7.1%
R39888
 
6.4%
N29930
 
4.8%
T28254
 
4.5%
C26338
 
4.2%
Other values (36)150034
24.1%

cnpj
Categorical

HIGH CARDINALITY

Distinct3788
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
16.404.287/0001-55
 
198
33.050.196/0001-88
 
140
02.709.449/0001-59
 
139
09.078.935/0001-65
 
134
08.070.566/0001-00
 
124
Other values (3783)
18830 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters352170
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1346 ?
Unique (%)6.9%

Sample

1st row56.024.581/0001-56
2nd row02.275.306/0001-86
3rd row02.275.306/0001-86
4th row02.275.306/0001-86
5th row46.722.831/0001-78
ValueCountFrequency (%)
16.404.287/0001-55198
 
1.0%
33.050.196/0001-88140
 
0.7%
02.709.449/0001-59139
 
0.7%
09.078.935/0001-65134
 
0.7%
08.070.566/0001-00124
 
0.6%
89.637.490/0001-45124
 
0.6%
02.016.439/0001-38122
 
0.6%
43.776.517/0001-80121
 
0.6%
42.150.391/0001-70119
 
0.6%
04.172.213/0001-51116
 
0.6%
Other values (3778)18228
93.2%
2021-08-18T23:55:53.002537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16.404.287/0001-55198
 
1.0%
33.050.196/0001-88140
 
0.7%
02.709.449/0001-59139
 
0.7%
09.078.935/0001-65134
 
0.7%
08.070.566/0001-00124
 
0.6%
89.637.490/0001-45124
 
0.6%
02.016.439/0001-38122
 
0.6%
43.776.517/0001-80121
 
0.6%
42.150.391/0001-70119
 
0.6%
04.172.213/0001-51116
 
0.6%
Other values (3778)18228
93.2%

Most occurring characters

ValueCountFrequency (%)
088241
25.1%
140904
11.6%
.39130
11.1%
/19565
 
5.6%
-19565
 
5.6%
418753
 
5.3%
318719
 
5.3%
518700
 
5.3%
618497
 
5.3%
217988
 
5.1%
Other values (3)52108
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number273910
77.8%
Other Punctuation58695
 
16.7%
Dash Punctuation19565
 
5.6%

Most frequent character per category

ValueCountFrequency (%)
088241
32.2%
140904
14.9%
418753
 
6.8%
318719
 
6.8%
518700
 
6.8%
618497
 
6.8%
217988
 
6.6%
717746
 
6.5%
917560
 
6.4%
816802
 
6.1%
ValueCountFrequency (%)
.39130
66.7%
/19565
33.3%
ValueCountFrequency (%)
-19565
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common352170
100.0%

Most frequent character per script

ValueCountFrequency (%)
088241
25.1%
140904
11.6%
.39130
11.1%
/19565
 
5.6%
-19565
 
5.6%
418753
 
5.3%
318719
 
5.3%
518700
 
5.3%
618497
 
5.3%
217988
 
5.1%
Other values (3)52108
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII352170
100.0%

Most frequent character per block

ValueCountFrequency (%)
088241
25.1%
140904
11.6%
.39130
11.1%
/19565
 
5.6%
-19565
 
5.6%
418753
 
5.3%
318719
 
5.3%
518700
 
5.3%
618497
 
5.3%
217988
 
5.1%
Other values (3)52108
14.8%

descricao_do_projeto
Categorical

HIGH CARDINALITY

Distinct5535
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
PROGRAMA DE MODERNIZACAO DA ADMINISTRACAO TRIBUTARIA E DAGESTAO DOS SETORES SOCIAIS BASICOS
 
177
CONTRATACAO DE CREDITO PARA AQUISICAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS.
 
132
SUPLEMENTACAO DE RECURSOS, VISANDO O EQUACIONAMENTO DE FUNDING PARA A IMPLANTACAO DAS QUATRO UNIDADES DO GRUPO.
 
122
APOIAR O PROGRAMA SENAI PARA A COMPETITIVIDADE INDUSTRIAL, MEDIANTE O FINANCIAMENTO DE CENTROS DE FORMACAO PROFISSIO- NAL, INSTITUTOS TECNOLOGICOS E DE INOVACAO, BEM COMO AQUISICAO DE UNIDADES, LIMITADO AO VALOR DO CREDITO".
 
92
CONSTRUCAO DA UHE SANTO ANTONIO, COM CAPACIDADE INSTALADA DEGERACAO DE 3.150 MW, NO RIO MADEIRA, NO MUNICIPIO DE PORTO VELHO/RO, BEM COMO DAS INSTALACOES DE TRANSMISSAO DE INTERESSE RESTRITO A CENTRAL GERADORA.
 
90
Other values (5530)
18952 

Length

Max length900
Median length180
Mean length209.8871965
Min length12

Characters and Unicode

Total characters4106443
Distinct characters71
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2143 ?
Unique (%)11.0%

Sample

1st rowPROGRAMA DE MODERNIZACAO DA ADMINISTRACAO TRIBUTARIA E DA GESTAO DOS SETORES SOCIAIS BASICOS
2nd rowPROJETO DIREITO E CIDADANIA; PROJETO DEMONSTRATIVO DA CADEIA PRODUTIVA DE MARICULTURA; E SISTEMATIZACAO DA METODOLOGIA DO PROGRAMA JOVEM EMPRESARIO.
3rd rowPROJETO DIREITO E CIDADANIA; PROJETO DEMONSTRATIVO DA CADEIA PRODUTIVA DE MARICULTURA; E SISTEMATIZACAO DA METODOLOGIA DO PROGRAMA JOVEM EMPRESARIO.
4th rowPROJETO DIREITO E CIDADANIA; PROJETO DEMONSTRATIVO DA CADEIA PRODUTIVA DE MARICULTURA; E SISTEMATIZACAO DA METODOLOGIA DO PROGRAMA JOVEM EMPRESARIO.
5th rowAQUISICAO DE EQUIPAMENTOS NACIONAIS E MOBILIARIO.
ValueCountFrequency (%)
PROGRAMA DE MODERNIZACAO DA ADMINISTRACAO TRIBUTARIA E DAGESTAO DOS SETORES SOCIAIS BASICOS 177
 
0.9%
CONTRATACAO DE CREDITO PARA AQUISICAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS. 132
 
0.7%
SUPLEMENTACAO DE RECURSOS, VISANDO O EQUACIONAMENTO DE FUNDING PARA A IMPLANTACAO DAS QUATRO UNIDADES DO GRUPO. 122
 
0.6%
APOIAR O PROGRAMA SENAI PARA A COMPETITIVIDADE INDUSTRIAL, MEDIANTE O FINANCIAMENTO DE CENTROS DE FORMACAO PROFISSIO- NAL, INSTITUTOS TECNOLOGICOS E DE INOVACAO, BEM COMO AQUISICAO DE UNIDADES, LIMITADO AO VALOR DO CREDITO". 92
 
0.5%
CONSTRUCAO DA UHE SANTO ANTONIO, COM CAPACIDADE INSTALADA DEGERACAO DE 3.150 MW, NO RIO MADEIRA, NO MUNICIPIO DE PORTO VELHO/RO, BEM COMO DAS INSTALACOES DE TRANSMISSAO DE INTERESSE RESTRITO A CENTRAL GERADORA. 90
 
0.5%
EXPANSAO, MODERNIZACAO E ADEQUACAO DO SISTEMA DE DISTRIBUICAO DE ENERGIA ELETRICA NAS AREAS DE CONCESSAO DAS DISTRIBUIDORAS DE ENERGIAS. 86
 
0.4%
IMPLEMENTACAO DO PLANO DE INVESTIMENTO EM EXPANSAO E MODERNIZACAO DO SISTEMA ELETRICO DA CIA. PAULISTA DE FORCA E LUZ S.A. (CPFL PAULISTA), ABRANGENDO O 2 SEMESTRE DE 2010 E O ANO DE 2011. 81
 
0.4%
PROGRAMA EMERGENCIAL E EXCEPCIONAL DE APOIO FINANCEIRO AS CONCESSIONARIAS DE SERV. PUBLICOS DE DISTRIBUICAO DE ENERGIAELETRICA, CONFORME DEC.DIR.625, DE 21/12/01, PARA SUPRIR IN-SUFICIENCIA DE REC. DA EMPRESA DEVIDO A REDUCAO DE RECEITA DURANTE A VIGENCIA DO PROG.DE REDUCAO DE CONSUMO DE ENERGIA.80
 
0.4%
IMPLANTACAO DO PROGRAMA DE DESPOLUICAO DO RIO TIETE -ETAPAII, NO ESTADO DE SAO PAULO. 80
 
0.4%
IMPLEMENTACAO DO PLANO DE INVESTIMENTO EM EXPANSAO E MODERNIZACAO DO SISTEMA ELETRICO DA RIO GRANDE ENERGIA S.A. (RGE), ABRANGENDO O 2 SEMESTRE DE 2010 E O ANO DE 2011. 72
 
0.4%
Other values (5525)18553
94.8%
2021-08-18T23:55:54.093002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de69347
 
12.9%
e24868
 
4.6%
do15607
 
2.9%
da15253
 
2.8%
a9027
 
1.7%
em8969
 
1.7%
no8901
 
1.7%
para7677
 
1.4%
implantacao6663
 
1.2%
com6309
 
1.2%
Other values (15396)365601
67.9%

Most occurring characters

ValueCountFrequency (%)
1102391
26.8%
A410821
 
10.0%
E323112
 
7.9%
O290538
 
7.1%
I219122
 
5.3%
D199271
 
4.9%
S178174
 
4.3%
R165640
 
4.0%
N163029
 
4.0%
C150624
 
3.7%
Other values (61)903721
22.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2800172
68.2%
Space Separator1102391
 
26.8%
Decimal Number89253
 
2.2%
Other Punctuation85684
 
2.1%
Dash Punctuation14188
 
0.3%
Close Punctuation7283
 
0.2%
Open Punctuation7070
 
0.2%
Lowercase Letter225
 
< 0.1%
Currency Symbol160
 
< 0.1%
Math Symbol17
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A410821
14.7%
E323112
11.5%
O290538
10.4%
I219122
 
7.8%
D199271
 
7.1%
S178174
 
6.4%
R165640
 
5.9%
N163029
 
5.8%
C150624
 
5.4%
T141430
 
5.1%
Other values (16)558411
19.9%
ValueCountFrequency (%)
a40
17.8%
o30
13.3%
c16
 
7.1%
e15
 
6.7%
p14
 
6.2%
t14
 
6.2%
d14
 
6.2%
m12
 
5.3%
l12
 
5.3%
i12
 
5.3%
Other values (6)46
20.4%
ValueCountFrequency (%)
,42509
49.6%
.27384
32.0%
/10275
 
12.0%
;2847
 
3.3%
:1240
 
1.4%
"969
 
1.1%
%201
 
0.2%
&129
 
0.2%
'71
 
0.1%
*48
 
0.1%
Other values (2)11
 
< 0.1%
ValueCountFrequency (%)
025734
28.8%
215783
17.7%
114993
16.8%
37035
 
7.9%
56357
 
7.1%
45197
 
5.8%
84477
 
5.0%
63732
 
4.2%
73411
 
3.8%
92534
 
2.8%
ValueCountFrequency (%)
+15
88.2%
=2
 
11.8%
ValueCountFrequency (%)
1102391
100.0%
ValueCountFrequency (%)
-14188
100.0%
ValueCountFrequency (%)
(7070
100.0%
ValueCountFrequency (%)
)7283
100.0%
ValueCountFrequency (%)
$160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2800397
68.2%
Common1306046
31.8%

Most frequent character per script

ValueCountFrequency (%)
A410821
14.7%
E323112
11.5%
O290538
10.4%
I219122
 
7.8%
D199271
 
7.1%
S178174
 
6.4%
R165640
 
5.9%
N163029
 
5.8%
C150624
 
5.4%
T141430
 
5.1%
Other values (32)558636
19.9%
ValueCountFrequency (%)
1102391
84.4%
,42509
 
3.3%
.27384
 
2.1%
025734
 
2.0%
215783
 
1.2%
114993
 
1.1%
-14188
 
1.1%
/10275
 
0.8%
)7283
 
0.6%
(7070
 
0.5%
Other values (19)38436
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4106443
100.0%

Most frequent character per block

ValueCountFrequency (%)
1102391
26.8%
A410821
 
10.0%
E323112
 
7.9%
O290538
 
7.1%
I219122
 
5.3%
D199271
 
4.9%
S178174
 
4.3%
R165640
 
4.0%
N163029
 
4.0%
C150624
 
3.7%
Other values (61)903721
22.0%

uf
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
SP
4586 
IE
2152 
RJ
1955 
MG
1507 
RS
1419 
Other values (23)
7946 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters39130
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowBA
3rd rowBA
4th rowBA
5th rowSP
ValueCountFrequency (%)
SP4586
23.4%
IE2152
11.0%
RJ1955
10.0%
MG1507
 
7.7%
RS1419
 
7.3%
SC1174
 
6.0%
PR1032
 
5.3%
BA1024
 
5.2%
GO594
 
3.0%
RN511
 
2.6%
Other values (18)3611
18.5%
2021-08-18T23:55:54.772687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp4586
23.4%
ie2152
11.0%
rj1955
10.0%
mg1507
 
7.7%
rs1419
 
7.3%
sc1174
 
6.0%
pr1032
 
5.3%
ba1024
 
5.2%
go594
 
3.0%
rn511
 
2.6%
Other values (18)3611
18.5%

Most occurring characters

ValueCountFrequency (%)
S7920
20.2%
P6731
17.2%
R5096
13.0%
E3462
8.8%
M2626
 
6.7%
I2374
 
6.1%
G2101
 
5.4%
J1955
 
5.0%
A1855
 
4.7%
C1722
 
4.4%
Other values (7)3288
8.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39130
100.0%

Most frequent character per category

ValueCountFrequency (%)
S7920
20.2%
P6731
17.2%
R5096
13.0%
E3462
8.8%
M2626
 
6.7%
I2374
 
6.1%
G2101
 
5.4%
J1955
 
5.0%
A1855
 
4.7%
C1722
 
4.4%
Other values (7)3288
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin39130
100.0%

Most frequent character per script

ValueCountFrequency (%)
S7920
20.2%
P6731
17.2%
R5096
13.0%
E3462
8.8%
M2626
 
6.7%
I2374
 
6.1%
G2101
 
5.4%
J1955
 
5.0%
A1855
 
4.7%
C1722
 
4.4%
Other values (7)3288
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII39130
100.0%

Most frequent character per block

ValueCountFrequency (%)
S7920
20.2%
P6731
17.2%
R5096
13.0%
E3462
8.8%
M2626
 
6.7%
I2374
 
6.1%
G2101
 
5.4%
J1955
 
5.0%
A1855
 
4.7%
C1722
 
4.4%
Other values (7)3288
8.4%

municipio
Categorical

HIGH CARDINALITY

Distinct1057
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
SEM MUNICÍPIO
6273 
RIO DE JANEIRO
1179 
SAO PAULO
 
897
ITAJAI
 
251
CURITIBA
 
175
Other values (1052)
10790 

Length

Max length29
Median length12
Mean length11.17674419
Min length3

Characters and Unicode

Total characters218673
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)1.4%

Sample

1st rowRIBEIRAO PRETO
2nd rowITUBERA
3rd rowITUBERA
4th rowITUBERA
5th rowFRANCA
ValueCountFrequency (%)
SEM MUNICÍPIO6273
32.1%
RIO DE JANEIRO1179
 
6.0%
SAO PAULO897
 
4.6%
ITAJAI251
 
1.3%
CURITIBA175
 
0.9%
NITEROI173
 
0.9%
CAMACARI161
 
0.8%
PORTO VELHO152
 
0.8%
CAMPINAS152
 
0.8%
PORTO ALEGRE148
 
0.8%
Other values (1047)10004
51.1%
2021-08-18T23:55:55.504311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
município6273
17.8%
sem6273
17.8%
sao1521
 
4.3%
de1449
 
4.1%
rio1412
 
4.0%
janeiro1179
 
3.3%
do935
 
2.6%
paulo898
 
2.5%
porto329
 
0.9%
sul320
 
0.9%
Other values (1066)14740
41.7%

Most occurring characters

ValueCountFrequency (%)
I24390
11.2%
A22449
10.3%
O21681
9.9%
15764
 
7.2%
E15665
 
7.2%
M15645
 
7.2%
S13397
 
6.1%
N13312
 
6.1%
U11662
 
5.3%
R11365
 
5.2%
Other values (20)53343
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter202866
92.8%
Space Separator15764
 
7.2%
Other Punctuation27
 
< 0.1%
Dash Punctuation16
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
I24390
12.0%
A22449
11.1%
O21681
10.7%
E15665
 
7.7%
M15645
 
7.7%
S13397
 
6.6%
N13312
 
6.6%
U11662
 
5.7%
R11365
 
5.6%
P10410
 
5.1%
Other values (17)42890
21.1%
ValueCountFrequency (%)
15764
100.0%
ValueCountFrequency (%)
'27
100.0%
ValueCountFrequency (%)
-16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin202866
92.8%
Common15807
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
I24390
12.0%
A22449
11.1%
O21681
10.7%
E15665
 
7.7%
M15645
 
7.7%
S13397
 
6.6%
N13312
 
6.6%
U11662
 
5.7%
R11365
 
5.6%
P10410
 
5.1%
Other values (17)42890
21.1%
ValueCountFrequency (%)
15764
99.7%
'27
 
0.2%
-16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII212399
97.1%
None6274
 
2.9%

Most frequent character per block

ValueCountFrequency (%)
I24390
11.5%
A22449
10.6%
O21681
10.2%
15764
 
7.4%
E15665
 
7.4%
M15645
 
7.4%
S13397
 
6.3%
N13312
 
6.3%
U11662
 
5.5%
R11365
 
5.4%
Other values (18)47069
22.2%
ValueCountFrequency (%)
Í6273
> 99.9%
Ã1
 
< 0.1%

municipio_codigo
Real number (ℝ≥0)

ZEROS

Distinct1063
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2858795.334
Minimum0
Maximum9999999
Zeros5357
Zeros (%)27.4%
Memory size153.0 KiB
2021-08-18T23:55:55.903513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3303302
Q34100509
95-th percentile9999999
Maximum9999999
Range9999999
Interquartile range (IQR)4100509

Descriptive statistics

Standard deviation2356910.317
Coefficient of variation (CV)0.824441781
Kurtosis1.905958375
Mean2858795.334
Median Absolute Deviation (MAD)914506
Skewness0.985368199
Sum5.593233071 × 1010
Variance5.555026241 × 1012
MonotocityNot monotonic
2021-08-18T23:55:56.314955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05357
27.4%
33045571179
 
6.0%
9999999984
 
5.0%
3550308897
 
4.6%
4208203251
 
1.3%
4106902175
 
0.9%
3303302173
 
0.9%
2905701161
 
0.8%
3509502152
 
0.8%
1100205152
 
0.8%
Other values (1053)10084
51.5%
ValueCountFrequency (%)
05357
27.4%
11000231
 
< 0.1%
11000491
 
< 0.1%
1100205152
 
0.8%
11002541
 
< 0.1%
ValueCountFrequency (%)
9999999984
5.0%
530010893
 
0.5%
52215511
 
< 0.1%
52207023
 
< 0.1%
52204053
 
< 0.1%

numero_do_contrato
Real number (ℝ≥0)

Distinct7065
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11043126.99
Minimum202711
Maximum99263321
Zeros0
Zeros (%)0.0%
Memory size153.0 KiB
2021-08-18T23:55:56.665932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum202711
5-th percentile2275541
Q17211021
median11205093
Q314206241
95-th percentile18571011.8
Maximum99263321
Range99060610
Interquartile range (IQR)6995220

Descriptive statistics

Standard deviation5641704.09
Coefficient of variation (CV)0.510879219
Kurtosis49.01334347
Mean11043126.99
Median Absolute Deviation (MAD)3003748
Skewness3.915225544
Sum2.160587795 × 1011
Variance3.182882504 × 1013
MonotocityNot monotonic
2021-08-18T23:55:57.000914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1220152181
 
0.4%
126203164
 
0.3%
327943160
 
0.3%
1620751156
 
0.3%
821120155
 
0.3%
720508145
 
0.2%
720019142
 
0.2%
820833140
 
0.2%
620580138
 
0.2%
424053138
 
0.2%
Other values (7055)19046
97.3%
ValueCountFrequency (%)
2027111
< 0.1%
2044711
< 0.1%
2088711
< 0.1%
2089511
< 0.1%
2101211
< 0.1%
ValueCountFrequency (%)
992633211
< 0.1%
992569711
< 0.1%
992519711
< 0.1%
992306312
< 0.1%
972216511
< 0.1%

data_da_contratacao
Categorical

HIGH CARDINALITY

Distinct2974
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
2010-12-20
 
216
2014-12-30
 
160
2015-12-15
 
125
2009-02-03
 
117
2013-12-30
 
114
Other values (2969)
18833 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters195650
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique732 ?
Unique (%)3.7%

Sample

1st row2002-01-02
2nd row2002-01-03
3rd row2002-01-03
4th row2002-01-03
5th row2002-01-09
ValueCountFrequency (%)
2010-12-20216
 
1.1%
2014-12-30160
 
0.8%
2015-12-15125
 
0.6%
2009-02-03117
 
0.6%
2013-12-30114
 
0.6%
2011-12-13106
 
0.5%
2011-03-31105
 
0.5%
2012-04-03100
 
0.5%
2002-08-0883
 
0.4%
2010-06-2982
 
0.4%
Other values (2964)18357
93.8%
2021-08-18T23:55:58.054996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2010-12-20216
 
1.1%
2014-12-30160
 
0.8%
2015-12-15125
 
0.6%
2009-02-03117
 
0.6%
2013-12-30114
 
0.6%
2011-12-13106
 
0.5%
2011-03-31105
 
0.5%
2012-04-03100
 
0.5%
2002-08-0883
 
0.4%
2010-06-2982
 
0.4%
Other values (2964)18357
93.8%

Most occurring characters

ValueCountFrequency (%)
050793
26.0%
-39130
20.0%
236127
18.5%
131026
15.9%
37524
 
3.8%
45777
 
3.0%
95586
 
2.9%
85369
 
2.7%
74958
 
2.5%
64902
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number156520
80.0%
Dash Punctuation39130
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
050793
32.5%
236127
23.1%
131026
19.8%
37524
 
4.8%
45777
 
3.7%
95586
 
3.6%
85369
 
3.4%
74958
 
3.2%
64902
 
3.1%
54458
 
2.8%
ValueCountFrequency (%)
-39130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common195650
100.0%

Most frequent character per script

ValueCountFrequency (%)
050793
26.0%
-39130
20.0%
236127
18.5%
131026
15.9%
37524
 
3.8%
45777
 
3.0%
95586
 
2.9%
85369
 
2.7%
74958
 
2.5%
64902
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII195650
100.0%

Most frequent character per block

ValueCountFrequency (%)
050793
26.0%
-39130
20.0%
236127
18.5%
131026
15.9%
37524
 
3.8%
45777
 
3.0%
95586
 
2.9%
85369
 
2.7%
74958
 
2.5%
64902
 
2.5%

valor_contratado_reais
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct15023
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46964792.21
Minimum229.12
Maximum9889997970
Zeros0
Zeros (%)0.0%
Memory size153.0 KiB
2021-08-18T23:55:58.402573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum229.12
5-th percentile300000
Q12385165.75
median9385000
Q330665000
95-th percentile174640200
Maximum9889997970
Range9889997741
Interquartile range (IQR)28279834.25

Descriptive statistics

Standard deviation203148552
Coefficient of variation (CV)4.325549894
Kurtosis731.9603358
Mean46964792.21
Median Absolute Deviation (MAD)8385000
Skewness21.17503098
Sum9.188661596 × 1011
Variance4.126933417 × 1016
MonotocityNot monotonic
2021-08-18T23:55:58.894301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000123
 
0.6%
20000000100
 
0.5%
100000073
 
0.4%
500000067
 
0.3%
300000057
 
0.3%
1500000053
 
0.3%
200000052
 
0.3%
50000045
 
0.2%
600000045
 
0.2%
5000000041
 
0.2%
Other values (15013)18909
96.6%
ValueCountFrequency (%)
229.121
< 0.1%
599.841
< 0.1%
3032.941
< 0.1%
61522
< 0.1%
80001
< 0.1%
ValueCountFrequency (%)
98899979701
< 0.1%
94099984971
< 0.1%
66912591001
< 0.1%
61502965801
< 0.1%
56999972441
< 0.1%

valor_desembolsado_reais
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct16238
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39465124.57
Minimum0
Maximum9889997970
Zeros1341
Zeros (%)6.9%
Memory size153.0 KiB
2021-08-18T23:55:59.498764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11402855
median7000000
Q325000000
95-th percentile141137310.1
Maximum9889997970
Range9889997970
Interquartile range (IQR)23597145

Descriptive statistics

Standard deviation185414979.6
Coefficient of variation (CV)4.69819826
Kurtosis989.5738057
Mean39465124.57
Median Absolute Deviation (MAD)6672595.61
Skewness24.74748587
Sum7.721351621 × 1011
Variance3.437871464 × 1016
MonotocityNot monotonic
2021-08-18T23:55:59.814414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01341
 
6.9%
1000000044
 
0.2%
2000000034
 
0.2%
100000029
 
0.1%
300000025
 
0.1%
5000000025
 
0.1%
200000023
 
0.1%
3000000022
 
0.1%
50000021
 
0.1%
800000019
 
0.1%
Other values (16228)17982
91.9%
ValueCountFrequency (%)
01341
6.9%
149.342
 
< 0.1%
298.681
 
< 0.1%
837.12
 
< 0.1%
13872
 
< 0.1%
ValueCountFrequency (%)
98899979701
< 0.1%
94099984971
< 0.1%
66912591001
< 0.1%
61502965801
< 0.1%
56999972441
< 0.1%

fonte_de_recurso_desembolsos
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
RECURSOS LIVRES - FAT
3551 
RECURSOS LIVRES - FAT / RECURSOS LIVRES - TESOURO
2629 
RECURSOS LIVRES - FAT / RECURSOS LIVRES - PRÓPRIOS / RECURSOS LIVRES - TESOURO
2600 
RECURSOS LIVRES - FAT / RECURSOS LIVRES - PRÓPRIOS
1405 
RECURSOS LIVRES - TESOURO
1173 
Other values (84)
8207 

Length

Max length184
Median length49
Mean length50.46552517
Min length1

Characters and Unicode

Total characters987358
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowRECURSOS VINCULADOS - PIS/PASEP
2nd rowRECURSOS ESTATUTÁRIOS - PRÓPRIOS ESTATUTÁRIOS
3rd rowRECURSOS ESTATUTÁRIOS - PRÓPRIOS ESTATUTÁRIOS
4th rowRECURSOS ESTATUTÁRIOS - PRÓPRIOS ESTATUTÁRIOS
5th rowRECURSOS LIVRES - FAT
ValueCountFrequency (%)
RECURSOS LIVRES - FAT3551
18.1%
RECURSOS LIVRES - FAT / RECURSOS LIVRES - TESOURO2629
13.4%
RECURSOS LIVRES - FAT / RECURSOS LIVRES - PRÓPRIOS / RECURSOS LIVRES - TESOURO2600
13.3%
RECURSOS LIVRES - FAT / RECURSOS LIVRES - PRÓPRIOS1405
 
7.2%
RECURSOS LIVRES - TESOURO1173
 
6.0%
RECURSOS ESTATUTÁRIOS - PRÓPRIOS ESTATUTÁRIOS1053
 
5.4%
RECURSOS VINCULADOS - TESOURO799
 
4.1%
RECURSOS LIVRES - FAT / RECURSOS LIVRES - TESOURO / RECURSOS VINCULADOS - FAT DEPÓSITOS ESPECIAIS776
 
4.0%
-722
 
3.7%
RECURSOS LIVRES - PRÓPRIOS718
 
3.7%
Other values (79)4139
21.2%
2021-08-18T23:56:00.568400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
53805
31.9%
recursos35963
21.3%
livres29973
17.8%
fat16306
 
9.7%
tesouro9506
 
5.6%
próprios8020
 
4.8%
vinculados4923
 
2.9%
especiais2731
 
1.6%
depósitos2731
 
1.6%
estatutários2134
 
1.3%
Other values (12)2390
 
1.4%

Most occurring characters

ValueCountFrequency (%)
148917
15.1%
S141040
14.3%
R129976
13.2%
E86006
8.7%
O73764
 
7.5%
I54321
 
5.5%
U52737
 
5.3%
C43757
 
4.4%
-36884
 
3.7%
T35148
 
3.6%
Other values (15)184808
18.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter784204
79.4%
Space Separator148917
 
15.1%
Dash Punctuation36884
 
3.7%
Other Punctuation17353
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
S141040
18.0%
R129976
16.6%
E86006
11.0%
O73764
9.4%
I54321
 
6.9%
U52737
 
6.7%
C43757
 
5.6%
T35148
 
4.5%
L35040
 
4.5%
V34896
 
4.4%
Other values (12)97519
12.4%
ValueCountFrequency (%)
148917
100.0%
ValueCountFrequency (%)
-36884
100.0%
ValueCountFrequency (%)
/17353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin784204
79.4%
Common203154
 
20.6%

Most frequent character per script

ValueCountFrequency (%)
S141040
18.0%
R129976
16.6%
E86006
11.0%
O73764
9.4%
I54321
 
6.9%
U52737
 
6.7%
C43757
 
5.6%
T35148
 
4.5%
L35040
 
4.5%
V34896
 
4.4%
Other values (12)97519
12.4%
ValueCountFrequency (%)
148917
73.3%
-36884
 
18.2%
/17353
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII974354
98.7%
None13004
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
148917
15.3%
S141040
14.5%
R129976
13.3%
E86006
8.8%
O73764
7.6%
I54321
 
5.6%
U52737
 
5.4%
C43757
 
4.5%
-36884
 
3.8%
T35148
 
3.6%
Other values (12)171804
17.6%
ValueCountFrequency (%)
Ó10751
82.7%
Á2134
 
16.4%
Ô119
 
0.9%

custo_financeiro
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
TJLP
10269 
US$ / CESTA
2539 
TAXA FIXA
2000 
TJ462
1312 
SEM CUSTO
1273 
Other values (24)
2172 

Length

Max length16
Median length4
Mean length5.804497828
Min length3

Characters and Unicode

Total characters113565
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st rowTJLP
2nd rowSEM CUSTO
3rd rowSEM CUSTO
4th rowSEM CUSTO
5th rowTJLP
ValueCountFrequency (%)
TJLP10269
52.5%
US$ / CESTA2539
 
13.0%
TAXA FIXA2000
 
10.2%
TJ4621312
 
6.7%
SEM CUSTO1273
 
6.5%
TLP974
 
5.0%
SELIC641
 
3.3%
IPCA422
 
2.2%
CDI36
 
0.2%
OUTROS27
 
0.1%
Other values (19)72
 
0.4%
2021-08-18T23:56:02.074393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tjlp10269
36.7%
us2542
 
9.1%
2539
 
9.1%
cesta2539
 
9.1%
taxa2003
 
7.2%
fixa2003
 
7.2%
tj4621312
 
4.7%
sem1273
 
4.6%
custo1273
 
4.6%
tlp974
 
3.5%
Other values (24)1236
 
4.4%

Most occurring characters

ValueCountFrequency (%)
T18447
16.2%
L11884
10.5%
P11665
10.3%
J11631
10.2%
A8970
7.9%
8398
7.4%
S8295
 
7.3%
C4930
 
4.3%
E4456
 
3.9%
X4006
 
3.5%
Other values (23)20883
18.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter95935
84.5%
Space Separator8398
 
7.4%
Decimal Number4086
 
3.6%
Other Punctuation2566
 
2.3%
Currency Symbol2542
 
2.2%
Lowercase Letter38
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
T18447
19.2%
L11884
12.4%
P11665
12.2%
J11631
12.1%
A8970
9.4%
S8295
8.6%
C4930
 
5.1%
E4456
 
4.6%
X4006
 
4.2%
U3842
 
4.0%
Other values (6)7809
8.1%
ValueCountFrequency (%)
41335
32.7%
61330
32.6%
21316
32.2%
334
 
0.8%
128
 
0.7%
524
 
0.6%
013
 
0.3%
73
 
0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
/2539
98.9%
%19
 
0.7%
,8
 
0.3%
ValueCountFrequency (%)
d19
50.0%
o19
50.0%
ValueCountFrequency (%)
8398
100.0%
ValueCountFrequency (%)
$2542
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin95973
84.5%
Common17592
 
15.5%

Most frequent character per script

ValueCountFrequency (%)
T18447
19.2%
L11884
12.4%
P11665
12.2%
J11631
12.1%
A8970
9.3%
S8295
8.6%
C4930
 
5.1%
E4456
 
4.6%
X4006
 
4.2%
U3842
 
4.0%
Other values (8)7847
8.2%
ValueCountFrequency (%)
8398
47.7%
$2542
 
14.4%
/2539
 
14.4%
41335
 
7.6%
61330
 
7.6%
21316
 
7.5%
334
 
0.2%
128
 
0.2%
524
 
0.1%
%19
 
0.1%
Other values (5)27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII113565
100.0%

Most frequent character per block

ValueCountFrequency (%)
T18447
16.2%
L11884
10.5%
P11665
10.3%
J11631
10.2%
A8970
7.9%
8398
7.4%
S8295
 
7.3%
C4930
 
4.3%
E4456
 
3.9%
X4006
 
3.5%
Other values (23)20883
18.4%

juros
Real number (ℝ≥0)

ZEROS

Distinct557
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.846882405
Minimum0
Maximum16.53
Zeros2045
Zeros (%)10.5%
Memory size153.0 KiB
2021-08-18T23:56:02.377888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.9
median2.5
Q33.67
95-th percentile6
Maximum16.53
Range16.53
Interquartile range (IQR)1.77

Descriptive statistics

Standard deviation1.823977691
Coefficient of variation (CV)0.6406930217
Kurtosis3.07986934
Mean2.846882405
Median Absolute Deviation (MAD)0.89
Skewness1.124511621
Sum55699.25426
Variance3.326894615
MonotocityNot monotonic
2021-08-18T23:56:02.638192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02045
 
10.5%
2.5756
 
3.9%
3.5606
 
3.1%
4.5528
 
2.7%
3521
 
2.7%
2463
 
2.4%
5.5421
 
2.2%
1.8368
 
1.9%
5364
 
1.9%
4356
 
1.8%
Other values (547)13137
67.1%
ValueCountFrequency (%)
02045
10.5%
0.00114
 
0.1%
0.0025
 
< 0.1%
0.011
 
< 0.1%
0.130
 
0.2%
ValueCountFrequency (%)
16.531
 
< 0.1%
16.31
 
< 0.1%
151
 
< 0.1%
13.4112
< 0.1%
134
< 0.1%

prazo_carencia_meses
Real number (ℝ≥0)

ZEROS

Distinct101
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.72537695
Minimum0
Maximum259
Zeros1850
Zeros (%)9.5%
Memory size153.0 KiB
2021-08-18T23:56:02.960899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median18
Q324
95-th percentile48
Maximum259
Range259
Interquartile range (IQR)12

Descriptive statistics

Standard deviation16.19024952
Coefficient of variation (CV)0.7811799787
Kurtosis9.459700203
Mean20.72537695
Median Absolute Deviation (MAD)6
Skewness2.010157058
Sum405492
Variance262.1241796
MonotocityNot monotonic
2021-08-18T23:56:03.341673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243772
19.3%
122576
13.2%
182298
11.7%
01850
 
9.5%
361344
 
6.9%
30891
 
4.6%
6821
 
4.2%
15479
 
2.4%
21316
 
1.6%
1290
 
1.5%
Other values (91)4928
25.2%
ValueCountFrequency (%)
01850
9.5%
1290
 
1.5%
2113
 
0.6%
3250
 
1.3%
4151
 
0.8%
ValueCountFrequency (%)
2591
 
< 0.1%
1821
 
< 0.1%
1813
< 0.1%
1501
 
< 0.1%
1441
 
< 0.1%

prazo_amortizacao_meses
Real number (ℝ≥0)

ZEROS

Distinct259
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.42412471
Minimum0
Maximum360
Zeros1283
Zeros (%)6.6%
Memory size153.0 KiB
2021-08-18T23:56:03.670932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q154
median72
Q3120
95-th percentile216
Maximum360
Range360
Interquartile range (IQR)66

Descriptive statistics

Standard deviation62.77057995
Coefficient of variation (CV)0.6941795693
Kurtosis-0.04111368693
Mean90.42412471
Median Absolute Deviation (MAD)24
Skewness0.7960090822
Sum1769148
Variance3940.145707
MonotocityNot monotonic
2021-08-18T23:56:03.937266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
602789
 
14.3%
721976
 
10.1%
1921289
 
6.6%
01283
 
6.6%
961132
 
5.8%
481128
 
5.8%
84866
 
4.4%
144500
 
2.6%
36458
 
2.3%
54450
 
2.3%
Other values (249)7694
39.3%
ValueCountFrequency (%)
01283
6.6%
1444
 
2.3%
24
 
< 0.1%
329
 
0.1%
48
 
< 0.1%
ValueCountFrequency (%)
3601
 
< 0.1%
3421
 
< 0.1%
3305
< 0.1%
3122
 
< 0.1%
3003
< 0.1%

modalidade_de_apoio
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
REEMBOLSÁVEL
18294 
NÃO REEMBOLSÁVEL
 
1271

Length

Max length16
Median length12
Mean length12.25985178
Min length12

Characters and Unicode

Total characters239864
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREEMBOLSÁVEL
2nd rowNÃO REEMBOLSÁVEL
3rd rowNÃO REEMBOLSÁVEL
4th rowNÃO REEMBOLSÁVEL
5th rowREEMBOLSÁVEL
ValueCountFrequency (%)
REEMBOLSÁVEL18294
93.5%
NÃO REEMBOLSÁVEL1271
 
6.5%
2021-08-18T23:56:04.421310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:04.586212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
reembolsável19565
93.9%
não1271
 
6.1%

Most occurring characters

ValueCountFrequency (%)
E58695
24.5%
L39130
16.3%
O20836
 
8.7%
R19565
 
8.2%
M19565
 
8.2%
B19565
 
8.2%
S19565
 
8.2%
Á19565
 
8.2%
V19565
 
8.2%
N1271
 
0.5%
Other values (2)2542
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter238593
99.5%
Space Separator1271
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
E58695
24.6%
L39130
16.4%
O20836
 
8.7%
R19565
 
8.2%
M19565
 
8.2%
B19565
 
8.2%
S19565
 
8.2%
Á19565
 
8.2%
V19565
 
8.2%
N1271
 
0.5%
ValueCountFrequency (%)
1271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin238593
99.5%
Common1271
 
0.5%

Most frequent character per script

ValueCountFrequency (%)
E58695
24.6%
L39130
16.4%
O20836
 
8.7%
R19565
 
8.2%
M19565
 
8.2%
B19565
 
8.2%
S19565
 
8.2%
Á19565
 
8.2%
V19565
 
8.2%
N1271
 
0.5%
ValueCountFrequency (%)
1271
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII219028
91.3%
None20836
 
8.7%

Most frequent character per block

ValueCountFrequency (%)
E58695
26.8%
L39130
17.9%
O20836
 
9.5%
R19565
 
8.9%
M19565
 
8.9%
B19565
 
8.9%
S19565
 
8.9%
V19565
 
8.9%
N1271
 
0.6%
1271
 
0.6%
ValueCountFrequency (%)
Á19565
93.9%
Ã1271
 
6.1%

forma_de_apoio
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
DIRETA
15457 
INDIRETA
4108 

Length

Max length8
Median length6
Mean length6.419933555
Min length6

Characters and Unicode

Total characters125606
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIRETA
2nd rowDIRETA
3rd rowDIRETA
4th rowDIRETA
5th rowINDIRETA
ValueCountFrequency (%)
DIRETA15457
79.0%
INDIRETA4108
 
21.0%
2021-08-18T23:56:05.331400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:05.650636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
direta15457
79.0%
indireta4108
 
21.0%

Most occurring characters

ValueCountFrequency (%)
I23673
18.8%
D19565
15.6%
R19565
15.6%
E19565
15.6%
T19565
15.6%
A19565
15.6%
N4108
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter125606
100.0%

Most frequent character per category

ValueCountFrequency (%)
I23673
18.8%
D19565
15.6%
R19565
15.6%
E19565
15.6%
T19565
15.6%
A19565
15.6%
N4108
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin125606
100.0%

Most frequent character per script

ValueCountFrequency (%)
I23673
18.8%
D19565
15.6%
R19565
15.6%
E19565
15.6%
T19565
15.6%
A19565
15.6%
N4108
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII125606
100.0%

Most frequent character per block

ValueCountFrequency (%)
I23673
18.8%
D19565
15.6%
R19565
15.6%
E19565
15.6%
T19565
15.6%
A19565
15.6%
N4108
 
3.3%

produto
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
BNDES FINEM
14261 
BNDES PROJECT FINANCE
1628 
BNDES LIMITE DE CRÉDITO
1537 
BNDES NÃO REEMBOLSÁVEL
 
1271
BNDES FINAME
 
262
Other values (10)
 
606

Length

Max length49
Median length11
Mean length13.85928955
Min length6

Characters and Unicode

Total characters271157
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowBNDES FINEM
2nd rowBNDES NÃO REEMBOLSÁVEL
3rd rowBNDES NÃO REEMBOLSÁVEL
4th rowBNDES NÃO REEMBOLSÁVEL
5th rowBNDES FINEM
ValueCountFrequency (%)
BNDES FINEM14261
72.9%
BNDES PROJECT FINANCE1628
 
8.3%
BNDES LIMITE DE CRÉDITO1537
 
7.9%
BNDES NÃO REEMBOLSÁVEL1271
 
6.5%
BNDES FINAME262
 
1.3%
BNDES EMPRÉSTIMO PONTE224
 
1.1%
BNDES DEBENTURES SIMPLES209
 
1.1%
BNDES MICROCRÉDITO59
 
0.3%
OPERAÇÃO FINANCEIRA40
 
0.2%
BNDES CRÉDITO DIRETO MÉDIAS EMPRESAS40
 
0.2%
Other values (5)34
 
0.2%
2021-08-18T23:56:06.261109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bndes19509
42.7%
finem14261
31.2%
finance1628
 
3.6%
project1628
 
3.6%
crédito1577
 
3.5%
de1538
 
3.4%
limite1537
 
3.4%
não1271
 
2.8%
reembolsável1271
 
2.8%
finame262
 
0.6%
Other values (18)1178
 
2.6%

Most occurring characters

ValueCountFrequency (%)
E45693
16.9%
N39076
14.4%
26095
9.6%
D22985
8.5%
S21776
8.0%
I21532
7.9%
B20994
7.7%
M18135
 
6.7%
F16192
 
6.0%
O6479
 
2.4%
Other values (20)32200
11.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter245034
90.4%
Space Separator26095
 
9.6%
Decimal Number24
 
< 0.1%
Dash Punctuation4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
E45693
18.6%
N39076
15.9%
D22985
9.4%
S21776
8.9%
I21532
8.8%
B20994
8.6%
M18135
 
7.4%
F16192
 
6.6%
O6479
 
2.6%
T5531
 
2.3%
Other values (16)26641
10.9%
ValueCountFrequency (%)
112
50.0%
012
50.0%
ValueCountFrequency (%)
26095
100.0%
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin245034
90.4%
Common26123
 
9.6%

Most frequent character per script

ValueCountFrequency (%)
E45693
18.6%
N39076
15.9%
D22985
9.4%
S21776
8.9%
I21532
8.8%
B20994
8.6%
M18135
 
7.4%
F16192
 
6.6%
O6479
 
2.6%
T5531
 
2.3%
Other values (16)26641
10.9%
ValueCountFrequency (%)
26095
99.9%
112
 
< 0.1%
012
 
< 0.1%
-4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII266629
98.3%
None4528
 
1.7%

Most frequent character per block

ValueCountFrequency (%)
E45693
17.1%
N39076
14.7%
26095
9.8%
D22985
8.6%
S21776
8.2%
I21532
8.1%
B20994
7.9%
M18135
 
6.8%
F16192
 
6.1%
O6479
 
2.4%
Other values (15)27672
10.4%
ValueCountFrequency (%)
É1900
42.0%
Ã1312
29.0%
Á1271
28.1%
Ç41
 
0.9%
Ó4
 
0.1%

instrumento_financeiro
Categorical

HIGH CARDINALITY

Distinct175
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
OUTROS
2398 
CAPACIDADE PRODUTIVA - Demais Indústrias e Agropecuária
2258 
FUNDO DA MARINHA MERCANTE
1266 
CAPACIDADE PRODUTIVA NA INDÚSTRIA,AGRICULTURA,COMÉRCIO E SERVIÇOS
 
978
PSI - BK - Demais Itens
 
977
Other values (170)
11688 

Length

Max length124
Median length25
Mean length30.87007411
Min length3

Characters and Unicode

Total characters603973
Distinct characters77
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.1%

Sample

1st rowBNDES PMAT
2nd rowFUNDO SOCIAL
3rd rowFUNDO SOCIAL
4th rowFUNDO SOCIAL
5th rowPROGRAMA IES
ValueCountFrequency (%)
OUTROS2398
 
12.3%
CAPACIDADE PRODUTIVA - Demais Indústrias e Agropecuária2258
 
11.5%
FUNDO DA MARINHA MERCANTE1266
 
6.5%
CAPACIDADE PRODUTIVA NA INDÚSTRIA,AGRICULTURA,COMÉRCIO E SERVIÇOS978
 
5.0%
PSI - BK - Demais Itens977
 
5.0%
ENERGIA - Distribuição de Energia Elétrica720
 
3.7%
INVESTIMENTO SOCIAL DE EMPRESAS (ISE)706
 
3.6%
LEILÃO DE INFRAESTRUTURA658
 
3.4%
ENERGIAS ALTERNATIVAS562
 
2.9%
ENERGIA - Geração de Energia Elétrica524
 
2.7%
Other values (165)8518
43.5%
2021-08-18T23:56:08.969968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8471
 
10.1%
e5336
 
6.3%
de4229
 
5.0%
produtiva3745
 
4.4%
capacidade3745
 
4.4%
demais3656
 
4.3%
energia3455
 
4.1%
outros2398
 
2.8%
agropecuária2311
 
2.7%
indústrias2258
 
2.7%
Other values (258)44653
53.0%

Most occurring characters

ValueCountFrequency (%)
64703
 
10.7%
A48940
 
8.1%
I39514
 
6.5%
E37325
 
6.2%
R28983
 
4.8%
D28578
 
4.7%
O27868
 
4.6%
S23471
 
3.9%
T21326
 
3.5%
N20706
 
3.4%
Other values (67)262559
43.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter384825
63.7%
Lowercase Letter140120
 
23.2%
Space Separator64703
 
10.7%
Dash Punctuation8792
 
1.5%
Other Punctuation3529
 
0.6%
Open Punctuation739
 
0.1%
Close Punctuation739
 
0.1%
Decimal Number526
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
A48940
12.7%
I39514
10.3%
E37325
9.7%
R28983
 
7.5%
D28578
 
7.4%
O27868
 
7.2%
S23471
 
6.1%
T21326
 
5.5%
N20706
 
5.4%
C20408
 
5.3%
Other values (25)87706
22.8%
ValueCountFrequency (%)
i17598
12.6%
e15685
11.2%
r14478
10.3%
a13934
9.9%
s12998
9.3%
o9636
 
6.9%
t7055
 
5.0%
n6557
 
4.7%
m5466
 
3.9%
d5423
 
3.9%
Other values (20)31290
22.3%
ValueCountFrequency (%)
3203
38.6%
2175
33.3%
164
 
12.2%
458
 
11.0%
526
 
4.9%
ValueCountFrequency (%)
,3493
99.0%
&36
 
1.0%
ValueCountFrequency (%)
-8532
97.0%
260
 
3.0%
ValueCountFrequency (%)
64703
100.0%
ValueCountFrequency (%)
(739
100.0%
ValueCountFrequency (%)
)739
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin524945
86.9%
Common79028
 
13.1%

Most frequent character per script

ValueCountFrequency (%)
A48940
 
9.3%
I39514
 
7.5%
E37325
 
7.1%
R28983
 
5.5%
D28578
 
5.4%
O27868
 
5.3%
S23471
 
4.5%
T21326
 
4.1%
N20706
 
3.9%
C20408
 
3.9%
Other values (55)227826
43.4%
ValueCountFrequency (%)
64703
81.9%
-8532
 
10.8%
,3493
 
4.4%
(739
 
0.9%
)739
 
0.9%
260
 
0.3%
3203
 
0.3%
2175
 
0.2%
164
 
0.1%
458
 
0.1%
Other values (2)62
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII580564
96.1%
None23149
 
3.8%
Punctuation260
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
64703
 
11.1%
A48940
 
8.4%
I39514
 
6.8%
E37325
 
6.4%
R28983
 
5.0%
D28578
 
4.9%
O27868
 
4.8%
S23471
 
4.0%
T21326
 
3.7%
N20706
 
3.6%
Other values (48)239150
41.2%
ValueCountFrequency (%)
á3323
14.4%
Ç3263
14.1%
Ã2716
11.7%
ú2384
10.3%
ç1901
8.2%
É1848
8.0%
é1782
7.7%
ã1759
7.6%
Ú1578
6.8%
Í1391
6.0%
Other values (8)1204
 
5.2%
ValueCountFrequency (%)
260
100.0%

inovacao
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
NÃO
18564 
SIM
 
1001

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58695
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÃO
2nd rowNÃO
3rd rowNÃO
4th rowNÃO
5th rowNÃO
ValueCountFrequency (%)
NÃO18564
94.9%
SIM1001
 
5.1%
2021-08-18T23:56:10.171269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:10.453774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
não18564
94.9%
sim1001
 
5.1%

Most occurring characters

ValueCountFrequency (%)
N18564
31.6%
Ã18564
31.6%
O18564
31.6%
S1001
 
1.7%
I1001
 
1.7%
M1001
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter58695
100.0%

Most frequent character per category

ValueCountFrequency (%)
N18564
31.6%
Ã18564
31.6%
O18564
31.6%
S1001
 
1.7%
I1001
 
1.7%
M1001
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin58695
100.0%

Most frequent character per script

ValueCountFrequency (%)
N18564
31.6%
Ã18564
31.6%
O18564
31.6%
S1001
 
1.7%
I1001
 
1.7%
M1001
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII40131
68.4%
None18564
31.6%

Most frequent character per block

ValueCountFrequency (%)
N18564
46.3%
O18564
46.3%
S1001
 
2.5%
I1001
 
2.5%
M1001
 
2.5%
ValueCountFrequency (%)
Ã18564
100.0%

area_operacional
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
AREA DE INDUSTRIA E SERVICOS
8997 
AREA DE ENERGIA
5236 
AREA DE GESTAO PUBLICA E SOCIOAMBIENTAL
2749 
AREA DE SANEAMENTO E TRANSPORTE
2346 
AREA DE OPERACOES E CANAIS DIGITAIS
 
128
Other values (4)
 
109

Length

Max length51
Median length28
Mean length26.54684385
Min length15

Characters and Unicode

Total characters519389
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAREA DE GESTAO PUBLICA E SOCIOAMBIENTAL
2nd rowAREA DE GESTAO PUBLICA E SOCIOAMBIENTAL
3rd rowAREA DE GESTAO PUBLICA E SOCIOAMBIENTAL
4th rowAREA DE GESTAO PUBLICA E SOCIOAMBIENTAL
5th rowAREA DE GESTAO PUBLICA E SOCIOAMBIENTAL
ValueCountFrequency (%)
AREA DE INDUSTRIA E SERVICOS8997
46.0%
AREA DE ENERGIA5236
26.8%
AREA DE GESTAO PUBLICA E SOCIOAMBIENTAL2749
 
14.1%
AREA DE SANEAMENTO E TRANSPORTE2346
 
12.0%
AREA DE OPERACOES E CANAIS DIGITAIS128
 
0.7%
AREA DE MERC CAP, PARTIC E REEST DE EMPRESAS50
 
0.3%
AREA DE PLANEJAMENTO ESTRATEGICO36
 
0.2%
AREA DE ESTRUTURACAO DE EMPRESAS E DESINVESTIMENTOS14
 
0.1%
AREA DE ESTRUTURACAO DE PARCERIAS DE INVESTIMENTOS9
 
< 0.1%
2021-08-18T23:56:12.091270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:12.325093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de19647
21.7%
area19565
21.6%
e14284
15.8%
industria8997
9.9%
servicos8997
9.9%
energia5236
 
5.8%
publica2749
 
3.0%
socioambiental2749
 
3.0%
gestao2749
 
3.0%
transporte2346
 
2.6%
Other values (15)3121
 
3.5%

Most occurring characters

ValueCountFrequency (%)
E86271
16.6%
A72245
13.9%
70875
13.6%
R47929
9.2%
I41127
7.9%
S37871
7.3%
D28786
 
5.5%
N24266
 
4.7%
O22310
 
4.3%
T21961
 
4.2%
Other values (10)65748
12.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter448464
86.3%
Space Separator70875
 
13.6%
Other Punctuation50
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
E86271
19.2%
A72245
16.1%
R47929
10.7%
I41127
9.2%
S37871
8.4%
D28786
 
6.4%
N24266
 
5.4%
O22310
 
5.0%
T21961
 
4.9%
C14969
 
3.3%
Other values (8)50729
11.3%
ValueCountFrequency (%)
70875
100.0%
ValueCountFrequency (%)
,50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin448464
86.3%
Common70925
 
13.7%

Most frequent character per script

ValueCountFrequency (%)
E86271
19.2%
A72245
16.1%
R47929
10.7%
I41127
9.2%
S37871
8.4%
D28786
 
6.4%
N24266
 
5.4%
O22310
 
5.0%
T21961
 
4.9%
C14969
 
3.3%
Other values (8)50729
11.3%
ValueCountFrequency (%)
70875
99.9%
,50
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII519389
100.0%

Most frequent character per block

ValueCountFrequency (%)
E86271
16.6%
A72245
13.9%
70875
13.6%
R47929
9.2%
I41127
7.9%
S37871
7.3%
D28786
 
5.5%
N24266
 
4.7%
O22310
 
4.3%
T21961
 
4.2%
Other values (10)65748
12.7%

setor_cnae
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
COMERCIO E SERVICOS
12263 
INDUSTRIA DE TRANSFORMAÇÃO
6387 
AGROPECUÁRIA E PESCA
 
729
INDUSTRIA EXTRATIVA
 
186

Length

Max length30
Median length30
Mean length30
Min length30

Characters and Unicode

Total characters586950
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMERCIO E SERVICOS
2nd rowCOMERCIO E SERVICOS
3rd rowCOMERCIO E SERVICOS
4th rowCOMERCIO E SERVICOS
5th rowCOMERCIO E SERVICOS
ValueCountFrequency (%)
COMERCIO E SERVICOS 12263
62.7%
INDUSTRIA DE TRANSFORMAÇÃO 6387
32.6%
AGROPECUÁRIA E PESCA 729
 
3.7%
INDUSTRIA EXTRATIVA 186
 
1.0%
2021-08-18T23:56:13.192624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:13.370521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
e12992
22.2%
comercio12263
21.0%
servicos12263
21.0%
industria6573
11.2%
de6387
10.9%
transformação6387
10.9%
pesca729
 
1.2%
agropecuária729
 
1.2%
extrativa186
 
0.3%

Most occurring characters

ValueCountFrequency (%)
208721
35.6%
O50292
 
8.6%
E45549
 
7.8%
R45517
 
7.8%
I38587
 
6.6%
C38247
 
6.5%
S38215
 
6.5%
A21906
 
3.7%
M18650
 
3.2%
T13332
 
2.3%
Other values (11)67934
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter378229
64.4%
Space Separator208721
35.6%

Most frequent character per category

ValueCountFrequency (%)
O50292
13.3%
E45549
12.0%
R45517
12.0%
I38587
10.2%
C38247
10.1%
S38215
10.1%
A21906
 
5.8%
M18650
 
4.9%
T13332
 
3.5%
N12960
 
3.4%
Other values (10)54974
14.5%
ValueCountFrequency (%)
208721
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin378229
64.4%
Common208721
35.6%

Most frequent character per script

ValueCountFrequency (%)
O50292
13.3%
E45549
12.0%
R45517
12.0%
I38587
10.2%
C38247
10.1%
S38215
10.1%
A21906
 
5.8%
M18650
 
4.9%
T13332
 
3.5%
N12960
 
3.4%
Other values (10)54974
14.5%
ValueCountFrequency (%)
208721
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII573447
97.7%
None13503
 
2.3%

Most frequent character per block

ValueCountFrequency (%)
208721
36.4%
O50292
 
8.8%
E45549
 
7.9%
R45517
 
7.9%
I38587
 
6.7%
C38247
 
6.7%
S38215
 
6.7%
A21906
 
3.8%
M18650
 
3.3%
T13332
 
2.3%
Other values (8)54431
 
9.5%
ValueCountFrequency (%)
Ç6387
47.3%
Ã6387
47.3%
Á729
 
5.4%

subsetor_cnae_agrupado
Categorical

HIGH CORRELATION

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
ELETRICIDADE E GÁS
4834 
PRODUTOS ALIMENTÍCIOS
1229 
TRANSPORTE AQUAVIÁRIO
1144 
COMÉRCIO
 
913
COQUE, PETRÓLEO E COMBUSTÍVEL
 
856
Other values (39)
10589 

Length

Max length31
Median length19
Mean length19.02029134
Min length6

Characters and Unicode

Total characters372132
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowADMINISTRAÇÃO PÚBLICA
2nd rowSAÚDE E SERV SOCIAL
3rd rowSAÚDE E SERV SOCIAL
4th rowSAÚDE E SERV SOCIAL
5th rowEDUCAÇÃO
ValueCountFrequency (%)
ELETRICIDADE E GÁS4834
24.7%
PRODUTOS ALIMENTÍCIOS1229
 
6.3%
TRANSPORTE AQUAVIÁRIO1144
 
5.8%
COMÉRCIO913
 
4.7%
COQUE, PETRÓLEO E COMBUSTÍVEL856
 
4.4%
ATIV AUX TRANSPORTE E ENTREGA855
 
4.4%
QUÍMICA732
 
3.7%
AGROPECUÁRIA729
 
3.7%
ADMINISTRAÇÃO PÚBLICA681
 
3.5%
METALURGIA630
 
3.2%
Other values (34)6962
35.6%
2021-08-18T23:56:14.110717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e10700
20.4%
gás4834
 
9.2%
eletricidade4834
 
9.2%
transporte2500
 
4.8%
ativ1609
 
3.1%
produtos1275
 
2.4%
alimentícios1229
 
2.3%
aquaviário1144
 
2.2%
comércio913
 
1.7%
coque856
 
1.6%
Other values (78)22591
43.0%

Most occurring characters

ValueCountFrequency (%)
E46736
12.6%
32920
 
8.8%
A30030
 
8.1%
I29868
 
8.0%
R26760
 
7.2%
O26594
 
7.1%
T23839
 
6.4%
C18668
 
5.0%
S18037
 
4.8%
L14954
 
4.0%
Other values (24)103726
27.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter335614
90.2%
Space Separator32920
 
8.8%
Other Punctuation3598
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
E46736
13.9%
A30030
 
8.9%
I29868
 
8.9%
R26760
 
8.0%
O26594
 
7.9%
T23839
 
7.1%
C18668
 
5.6%
S18037
 
5.4%
L14954
 
4.5%
D13395
 
4.0%
Other values (21)86733
25.8%
ValueCountFrequency (%)
,3591
99.8%
.7
 
0.2%
ValueCountFrequency (%)
32920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin335614
90.2%
Common36518
 
9.8%

Most frequent character per script

ValueCountFrequency (%)
E46736
13.9%
A30030
 
8.9%
I29868
 
8.9%
R26760
 
8.0%
O26594
 
7.9%
T23839
 
7.1%
C18668
 
5.6%
S18037
 
5.4%
L14954
 
4.5%
D13395
 
4.0%
Other values (21)86733
25.8%
ValueCountFrequency (%)
32920
90.1%
,3591
 
9.8%
.7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII351197
94.4%
None20935
 
5.6%

Most frequent character per block

ValueCountFrequency (%)
E46736
13.3%
32920
 
9.4%
A30030
 
8.6%
I29868
 
8.5%
R26760
 
7.6%
O26594
 
7.6%
T23839
 
6.8%
C18668
 
5.3%
S18037
 
5.1%
L14954
 
4.3%
Other values (15)82791
23.6%
ValueCountFrequency (%)
Á8198
39.2%
Í3578
17.1%
Ã2708
 
12.9%
Ç2704
 
12.9%
Ú1210
 
5.8%
Ó1053
 
5.0%
É931
 
4.4%
Ê368
 
1.8%
Õ185
 
0.9%

subsetor_cnae_codigo
Categorical

HIGH CARDINALITY

Distinct626
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
D3511587
 
1467
D3514000
 
1263
C1931400
 
783
O8411600
 
645
D3511581
 
516
Other values (621)
14891 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters156520
Distinct characters30
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)0.5%

Sample

1st rowO8411600
2nd rowQ8700000
3rd rowQ8700000
4th rowQ8700000
5th rowP8531700
ValueCountFrequency (%)
D35115871467
 
7.5%
D35140001263
 
6.5%
C1931400783
 
4.0%
O8411600645
 
3.3%
D3511581516
 
2.6%
D3512300480
 
2.5%
H5030101475
 
2.4%
C1071600467
 
2.4%
D3511582399
 
2.0%
H5221400391
 
2.0%
Other values (616)12679
64.8%
2021-08-18T23:56:15.001036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d35115871467
 
7.5%
d35140001263
 
6.5%
c1931400783
 
4.0%
o8411600645
 
3.3%
d3511581516
 
2.6%
d3512300480
 
2.5%
h5030101475
 
2.4%
c1071600467
 
2.4%
d3511582399
 
2.0%
h5221400391
 
2.0%
Other values (616)12679
64.8%

Most occurring characters

ValueCountFrequency (%)
040113
25.6%
129601
18.9%
212858
 
8.2%
512356
 
7.9%
311562
 
7.4%
48624
 
5.5%
C6387
 
4.1%
96018
 
3.8%
85969
 
3.8%
65041
 
3.2%
Other values (20)17991
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number136955
87.5%
Uppercase Letter19565
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
C6387
32.6%
D4834
24.7%
H2311
 
11.8%
G913
 
4.7%
J731
 
3.7%
A729
 
3.7%
O681
 
3.5%
E596
 
3.0%
R479
 
2.4%
Q343
 
1.8%
Other values (10)1561
 
8.0%
ValueCountFrequency (%)
040113
29.3%
129601
21.6%
212858
 
9.4%
512356
 
9.0%
311562
 
8.4%
48624
 
6.3%
96018
 
4.4%
85969
 
4.4%
65041
 
3.7%
74813
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common136955
87.5%
Latin19565
 
12.5%

Most frequent character per script

ValueCountFrequency (%)
C6387
32.6%
D4834
24.7%
H2311
 
11.8%
G913
 
4.7%
J731
 
3.7%
A729
 
3.7%
O681
 
3.5%
E596
 
3.0%
R479
 
2.4%
Q343
 
1.8%
Other values (10)1561
 
8.0%
ValueCountFrequency (%)
040113
29.3%
129601
21.6%
212858
 
9.4%
512356
 
9.0%
311562
 
8.4%
48624
 
6.3%
96018
 
4.4%
85969
 
4.4%
65041
 
3.7%
74813
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII156520
100.0%

Most frequent character per block

ValueCountFrequency (%)
040113
25.6%
129601
18.9%
212858
 
8.2%
512356
 
7.9%
311562
 
7.4%
48624
 
5.5%
C6387
 
4.1%
96018
 
3.8%
85969
 
3.8%
65041
 
3.2%
Other values (20)17991
11.5%

subsetor_cnae_nome
Categorical

HIGH CARDINALITY

Distinct614
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
GERACAO DE ENERGIA ELETRICA - EOLICA
 
1467
DISTRIBUICAO DE ENERGIA ELETRICA
 
1263
FABRICACAO DE ALCOOL
 
783
ADMINISTRACAO PUBLICA EM GERAL
 
645
GERACAO DE ENERGIA ELETRICA - HIDRELETRICA
 
516
Other values (609)
14891 

Length

Max length65
Median length65
Mean length65
Min length65

Characters and Unicode

Total characters1271725
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.5%

Sample

1st rowADMINISTRACAO PUBLICA EM GERAL
2nd rowATIV ATENCAO A SAUDE HUMANA INTEGR C/ASSIST SOC PREST RESID COL E
3rd rowATIV ATENCAO A SAUDE HUMANA INTEGR C/ASSIST SOC PREST RESID COL E
4th rowATIV ATENCAO A SAUDE HUMANA INTEGR C/ASSIST SOC PREST RESID COL E
5th rowEDUCACAO SUPERIOR - GRADUACAO
ValueCountFrequency (%)
GERACAO DE ENERGIA ELETRICA - EOLICA 1467
 
7.5%
DISTRIBUICAO DE ENERGIA ELETRICA 1263
 
6.5%
FABRICACAO DE ALCOOL 783
 
4.0%
ADMINISTRACAO PUBLICA EM GERAL 645
 
3.3%
GERACAO DE ENERGIA ELETRICA - HIDRELETRICA 516
 
2.6%
TRANSMISSAO DE ENERGIA ELETRICA 480
 
2.5%
NAVEGACAO DE APOIO MARITIMO 475
 
2.4%
FABRICACAO DE ACUCAR EM BRUTO 467
 
2.4%
GERACAO DE ENERGIA ELETRICA - PCH 399
 
2.0%
CONCESSIONARIAS RODOVIAS, PONTES, TUNEIS E SERVICOS RELACIONADOS 391
 
2.0%
Other values (604)12679
64.8%
2021-08-18T23:56:15.814998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de17562
 
15.5%
e5011
 
4.4%
energia4738
 
4.2%
eletrica4737
 
4.2%
fabricacao4614
 
4.1%
3414
 
3.0%
geracao2978
 
2.6%
distribuicao1645
 
1.5%
eolica1467
 
1.3%
em1400
 
1.2%
Other values (1110)65816
58.0%

Most occurring characters

ValueCountFrequency (%)
566004
44.5%
A100867
 
7.9%
E91008
 
7.2%
O65851
 
5.2%
I60633
 
4.8%
R58325
 
4.6%
C52877
 
4.2%
S40825
 
3.2%
T37248
 
2.9%
D33969
 
2.7%
Other values (21)164118
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter696222
54.7%
Space Separator566004
44.5%
Dash Punctuation5614
 
0.4%
Other Punctuation3513
 
0.3%
Open Punctuation186
 
< 0.1%
Close Punctuation186
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A100867
14.5%
E91008
13.1%
O65851
9.5%
I60633
8.7%
R58325
8.4%
C52877
7.6%
S40825
 
5.9%
T37248
 
5.4%
D33969
 
4.9%
N25997
 
3.7%
Other values (14)128622
18.5%
ValueCountFrequency (%)
,2535
72.2%
/873
 
24.9%
;105
 
3.0%
ValueCountFrequency (%)
566004
100.0%
ValueCountFrequency (%)
-5614
100.0%
ValueCountFrequency (%)
(186
100.0%
ValueCountFrequency (%)
)186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin696222
54.7%
Common575503
45.3%

Most frequent character per script

ValueCountFrequency (%)
A100867
14.5%
E91008
13.1%
O65851
9.5%
I60633
8.7%
R58325
8.4%
C52877
7.6%
S40825
 
5.9%
T37248
 
5.4%
D33969
 
4.9%
N25997
 
3.7%
Other values (14)128622
18.5%
ValueCountFrequency (%)
566004
98.3%
-5614
 
1.0%
,2535
 
0.4%
/873
 
0.2%
(186
 
< 0.1%
)186
 
< 0.1%
;105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1271725
100.0%

Most frequent character per block

ValueCountFrequency (%)
566004
44.5%
A100867
 
7.9%
E91008
 
7.2%
O65851
 
5.2%
I60633
 
4.8%
R58325
 
4.6%
C52877
 
4.2%
S40825
 
3.2%
T37248
 
2.9%
D33969
 
2.7%
Other values (21)164118
 
12.9%

setor_bndes
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
INFRAESTRUTURA
8194 
INDUSTRIA
6573 
COMERCIO/SERVICOS
4069 
AGROPECUÁRIA
 
729

Length

Max length17
Median length14
Mean length12.86961411
Min length9

Characters and Unicode

Total characters251794
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMERCIO/SERVICOS
2nd rowCOMERCIO/SERVICOS
3rd rowCOMERCIO/SERVICOS
4th rowCOMERCIO/SERVICOS
5th rowCOMERCIO/SERVICOS
ValueCountFrequency (%)
INFRAESTRUTURA8194
41.9%
INDUSTRIA6573
33.6%
COMERCIO/SERVICOS4069
20.8%
AGROPECUÁRIA729
 
3.7%
2021-08-18T23:56:16.566531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:16.892421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
infraestrutura8194
41.9%
industria6573
33.6%
comercio/servicos4069
20.8%
agropecuária729
 
3.7%

Most occurring characters

ValueCountFrequency (%)
R40751
16.2%
I30207
12.0%
A24419
9.7%
U23690
9.4%
T22961
9.1%
S22905
9.1%
E17061
6.8%
N14767
 
5.9%
C12936
 
5.1%
O12936
 
5.1%
Other values (8)29161
11.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter247725
98.4%
Other Punctuation4069
 
1.6%

Most frequent character per category

ValueCountFrequency (%)
R40751
16.5%
I30207
12.2%
A24419
9.9%
U23690
9.6%
T22961
9.3%
S22905
9.2%
E17061
6.9%
N14767
 
6.0%
C12936
 
5.2%
O12936
 
5.2%
Other values (7)25092
10.1%
ValueCountFrequency (%)
/4069
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin247725
98.4%
Common4069
 
1.6%

Most frequent character per script

ValueCountFrequency (%)
R40751
16.5%
I30207
12.2%
A24419
9.9%
U23690
9.6%
T22961
9.3%
S22905
9.2%
E17061
6.9%
N14767
 
6.0%
C12936
 
5.2%
O12936
 
5.2%
Other values (7)25092
10.1%
ValueCountFrequency (%)
/4069
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII251065
99.7%
None729
 
0.3%

Most frequent character per block

ValueCountFrequency (%)
R40751
16.2%
I30207
12.0%
A24419
9.7%
U23690
9.4%
T22961
9.1%
S22905
9.1%
E17061
6.8%
N14767
 
5.9%
C12936
 
5.2%
O12936
 
5.2%
Other values (7)28432
11.3%
ValueCountFrequency (%)
Á729
100.0%

subsetor_bndes
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
ENERGIA ELÉTRICA
4727 
COMÉRCIO E SERVIÇOS
4069 
QUÍMICA E PETROQUÍMICA
1833 
ALIMENTO E BEBIDA
1359 
OUTROS TRANSPORTES
1233 
Other values (14)
6344 

Length

Max length23
Median length18
Mean length17.50672119
Min length6

Characters and Unicode

Total characters342519
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMÉRCIO E SERVIÇOS
2nd rowCOMÉRCIO E SERVIÇOS
3rd rowCOMÉRCIO E SERVIÇOS
4th rowCOMÉRCIO E SERVIÇOS
5th rowCOMÉRCIO E SERVIÇOS
ValueCountFrequency (%)
ENERGIA ELÉTRICA4727
24.2%
COMÉRCIO E SERVIÇOS4069
20.8%
QUÍMICA E PETROQUÍMICA1833
 
9.4%
ALIMENTO E BEBIDA1359
 
6.9%
OUTROS TRANSPORTES1233
 
6.3%
ATV. AUX. TRANSPORTES855
 
4.4%
METALURGIA E PRODUTOS744
 
3.8%
AGROPECUÁRIA729
 
3.7%
MATERIAL DE TRANSPORTE705
 
3.6%
SERV. UTILIDADE PÚBLICA703
 
3.6%
Other values (9)2608
13.3%
2021-08-18T23:56:17.661670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e8604
18.1%
energia4727
 
10.0%
elétrica4727
 
10.0%
serviços4069
 
8.6%
comércio4069
 
8.6%
transportes2088
 
4.4%
petroquímica1833
 
3.9%
química1833
 
3.9%
bebida1359
 
2.9%
alimento1359
 
2.9%
Other values (23)12814
27.0%

Most occurring characters

ValueCountFrequency (%)
E41519
12.1%
R33015
 
9.6%
I29915
 
8.7%
A28440
 
8.3%
27917
 
8.2%
O25585
 
7.5%
T21017
 
6.1%
C19925
 
5.8%
S17664
 
5.2%
M11185
 
3.3%
Other values (21)86337
25.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter312189
91.1%
Space Separator27917
 
8.2%
Other Punctuation2413
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
E41519
13.3%
R33015
10.6%
I29915
9.6%
A28440
 
9.1%
O25585
 
8.2%
T21017
 
6.7%
C19925
 
6.4%
S17664
 
5.7%
M11185
 
3.6%
L10545
 
3.4%
Other values (19)73379
23.5%
ValueCountFrequency (%)
27917
100.0%
ValueCountFrequency (%)
.2413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin312189
91.1%
Common30330
 
8.9%

Most frequent character per script

ValueCountFrequency (%)
E41519
13.3%
R33015
10.6%
I29915
9.6%
A28440
 
9.1%
O25585
 
8.2%
T21017
 
6.7%
C19925
 
6.4%
S17664
 
5.7%
M11185
 
3.6%
L10545
 
3.4%
Other values (19)73379
23.5%
ValueCountFrequency (%)
27917
92.0%
.2413
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII322592
94.2%
None19927
 
5.8%

Most frequent character per block

ValueCountFrequency (%)
E41519
12.9%
R33015
10.2%
I29915
9.3%
A28440
8.8%
27917
8.7%
O25585
 
7.9%
T21017
 
6.5%
C19925
 
6.2%
S17664
 
5.5%
M11185
 
3.5%
Other values (12)66410
20.6%
ValueCountFrequency (%)
É8796
44.1%
Ç4522
22.7%
Í3666
18.4%
Á1141
 
5.7%
Ú703
 
3.5%
Â457
 
2.3%
Ã268
 
1.3%
Ê189
 
0.9%
Õ185
 
0.9%

porte_do_cliente
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
GRANDE
17101 
MÉDIA
 
1188
PEQUENA
 
731
MICRO
 
545

Length

Max length7
Median length6
Mean length5.948786098
Min length5

Characters and Unicode

Total characters116388
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGRANDE
2nd rowMICRO
3rd rowMICRO
4th rowMICRO
5th rowGRANDE
ValueCountFrequency (%)
GRANDE17101
87.4%
MÉDIA1188
 
6.1%
PEQUENA731
 
3.7%
MICRO545
 
2.8%
2021-08-18T23:56:18.516797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:18.739961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
grande17101
87.4%
média1188
 
6.1%
pequena731
 
3.7%
micro545
 
2.8%

Most occurring characters

ValueCountFrequency (%)
A19020
16.3%
E18563
15.9%
D18289
15.7%
N17832
15.3%
R17646
15.2%
G17101
14.7%
M1733
 
1.5%
I1733
 
1.5%
É1188
 
1.0%
P731
 
0.6%
Other values (4)2552
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter116388
100.0%

Most frequent character per category

ValueCountFrequency (%)
A19020
16.3%
E18563
15.9%
D18289
15.7%
N17832
15.3%
R17646
15.2%
G17101
14.7%
M1733
 
1.5%
I1733
 
1.5%
É1188
 
1.0%
P731
 
0.6%
Other values (4)2552
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin116388
100.0%

Most frequent character per script

ValueCountFrequency (%)
A19020
16.3%
E18563
15.9%
D18289
15.7%
N17832
15.3%
R17646
15.2%
G17101
14.7%
M1733
 
1.5%
I1733
 
1.5%
É1188
 
1.0%
P731
 
0.6%
Other values (4)2552
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII115200
99.0%
None1188
 
1.0%

Most frequent character per block

ValueCountFrequency (%)
A19020
16.5%
E18563
16.1%
D18289
15.9%
N17832
15.5%
R17646
15.3%
G17101
14.8%
M1733
 
1.5%
I1733
 
1.5%
P731
 
0.6%
Q731
 
0.6%
Other values (3)1821
 
1.6%
ValueCountFrequency (%)
É1188
100.0%

natureza_do_cliente
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
PRIVADA
17695 
PÚBLICA INDIRETA
 
872
ADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO MUNICIPAL
 
574
ADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO ESTADUAL
 
419
ADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO FEDERAL
 
5

Length

Max length48
Median length20
Mean length21.22806031
Min length16

Characters and Unicode

Total characters415327
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO MUNICIPAL
2nd rowPRIVADA
3rd rowPRIVADA
4th rowPRIVADA
5th rowPRIVADA
ValueCountFrequency (%)
PRIVADA 17695
90.4%
PÚBLICA INDIRETA872
 
4.5%
ADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO MUNICIPAL574
 
2.9%
ADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO ESTADUAL419
 
2.1%
ADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO FEDERAL5
 
< 0.1%
2021-08-18T23:56:19.496204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:19.658518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
privada17695
69.6%
pública1870
 
7.4%
governo998
 
3.9%
direta998
 
3.9%
administração998
 
3.9%
998
 
3.9%
indireta872
 
3.4%
municipal574
 
2.3%
estadual419
 
1.6%
federal5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
235897
56.8%
A42543
 
10.2%
I25451
 
6.1%
R21566
 
5.2%
D20987
 
5.1%
P20139
 
4.8%
V18693
 
4.5%
N3442
 
0.8%
E3297
 
0.8%
T3287
 
0.8%
Other values (13)20025
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator235897
56.8%
Uppercase Letter178432
43.0%
Dash Punctuation998
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
A42543
23.8%
I25451
14.3%
R21566
12.1%
D20987
11.8%
P20139
11.3%
V18693
10.5%
N3442
 
1.9%
E3297
 
1.8%
T3287
 
1.8%
O2994
 
1.7%
Other values (11)16033
 
9.0%
ValueCountFrequency (%)
235897
100.0%
ValueCountFrequency (%)
-998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common236895
57.0%
Latin178432
43.0%

Most frequent character per script

ValueCountFrequency (%)
A42543
23.8%
I25451
14.3%
R21566
12.1%
D20987
11.8%
P20139
11.3%
V18693
10.5%
N3442
 
1.9%
E3297
 
1.8%
T3287
 
1.8%
O2994
 
1.7%
Other values (11)16033
 
9.0%
ValueCountFrequency (%)
235897
99.6%
-998
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII411461
99.1%
None3866
 
0.9%

Most frequent character per block

ValueCountFrequency (%)
235897
57.3%
A42543
 
10.3%
I25451
 
6.2%
R21566
 
5.2%
D20987
 
5.1%
P20139
 
4.9%
V18693
 
4.5%
N3442
 
0.8%
E3297
 
0.8%
T3287
 
0.8%
Other values (10)16159
 
3.9%
ValueCountFrequency (%)
Ú1870
48.4%
Ç998
25.8%
Ã998
25.8%

instituicao_financeira_credenciada
Categorical

HIGH CORRELATION

Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
----------
15457 
ITAU UNIBANCO S.A.
 
1221
BANCO DO BRASIL SA
 
687
BANCO BRADESCO S.A.
 
543
BANCO SANTANDER (BRASIL) S.A.
 
357
Other values (42)
 
1300

Length

Max length55
Median length10
Mean length12.98706875
Min length10

Characters and Unicode

Total characters254092
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row----------
2nd row----------
3rd row----------
4th row----------
5th rowBANCO BRADESCO S.A.
ValueCountFrequency (%)
----------15457
79.0%
ITAU UNIBANCO S.A.1221
 
6.2%
BANCO DO BRASIL SA687
 
3.5%
BANCO BRADESCO S.A.543
 
2.8%
BANCO SANTANDER (BRASIL) S.A.357
 
1.8%
BANCO REGIONAL DE DESENVOLVIMENTO DO EXTREMO SUL320
 
1.6%
CAIXA ECONOMICA FEDERAL188
 
1.0%
BANCO VOTORANTIM S.A.156
 
0.8%
BADESUL DESENVOLVIMENTO S.A. - AGENCIA DE FOMENTO/RS104
 
0.5%
BANCO SAFRA S A97
 
0.5%
Other values (37)435
 
2.2%
2021-08-18T23:56:20.598485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15657
49.0%
s.a2655
 
8.3%
banco2591
 
8.1%
do1273
 
4.0%
unibanco1221
 
3.8%
itau1221
 
3.8%
brasil1163
 
3.6%
sa800
 
2.5%
de679
 
2.1%
bradesco545
 
1.7%
Other values (85)4156
 
13.0%

Most occurring characters

ValueCountFrequency (%)
-154770
60.9%
A13543
 
5.3%
12396
 
4.9%
O8550
 
3.4%
N8164
 
3.2%
S7252
 
2.9%
B5828
 
2.3%
I5649
 
2.2%
E5445
 
2.1%
.5302
 
2.1%
Other values (20)27193
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation154770
60.9%
Uppercase Letter80757
31.8%
Space Separator12396
 
4.9%
Other Punctuation5447
 
2.1%
Open Punctuation361
 
0.1%
Close Punctuation361
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
A13543
16.8%
O8550
10.6%
N8164
10.1%
S7252
9.0%
B5828
7.2%
I5649
7.0%
E5445
 
6.7%
C5120
 
6.3%
D3902
 
4.8%
R3577
 
4.4%
Other values (14)13727
17.0%
ValueCountFrequency (%)
.5302
97.3%
/145
 
2.7%
ValueCountFrequency (%)
-154770
100.0%
ValueCountFrequency (%)
12396
100.0%
ValueCountFrequency (%)
(361
100.0%
ValueCountFrequency (%)
)361
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common173335
68.2%
Latin80757
31.8%

Most frequent character per script

ValueCountFrequency (%)
A13543
16.8%
O8550
10.6%
N8164
10.1%
S7252
9.0%
B5828
7.2%
I5649
7.0%
E5445
 
6.7%
C5120
 
6.3%
D3902
 
4.8%
R3577
 
4.4%
Other values (14)13727
17.0%
ValueCountFrequency (%)
-154770
89.3%
12396
 
7.2%
.5302
 
3.1%
(361
 
0.2%
)361
 
0.2%
/145
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII254092
100.0%

Most frequent character per block

ValueCountFrequency (%)
-154770
60.9%
A13543
 
5.3%
12396
 
4.9%
O8550
 
3.4%
N8164
 
3.2%
S7252
 
2.9%
B5828
 
2.3%
I5649
 
2.2%
E5445
 
2.1%
.5302
 
2.1%
Other values (20)27193
 
10.7%
Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
----------
15457 
60701190000104
 
1221
00000000000191
 
687
60746948000112
 
543
90400888000142
 
357
Other values (42)
 
1300

Length

Max length14
Median length10
Mean length10.83986711
Min length10

Characters and Unicode

Total characters212082
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row----------
2nd row----------
3rd row----------
4th row----------
5th row60746948000112
ValueCountFrequency (%)
----------15457
79.0%
607011900001041221
 
6.2%
00000000000191687
 
3.5%
60746948000112543
 
2.8%
90400888000142357
 
1.8%
92816560000137320
 
1.6%
00360305000104188
 
1.0%
59588111000103156
 
0.8%
02885855000172104
 
0.5%
5816078900012897
 
0.5%
Other values (37)435
 
2.2%
2021-08-18T23:56:21.402617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15457
79.0%
607011900001041221
 
6.2%
00000000000191687
 
3.5%
60746948000112543
 
2.8%
90400888000142357
 
1.8%
92816560000137320
 
1.6%
00360305000104188
 
1.0%
59588111000103156
 
0.8%
02885855000172104
 
0.5%
5816078900012897
 
0.5%
Other values (37)435
 
2.2%

Most occurring characters

ValueCountFrequency (%)
-154570
72.9%
026599
 
12.5%
18994
 
4.2%
63807
 
1.8%
93710
 
1.7%
43534
 
1.7%
83236
 
1.5%
72921
 
1.4%
21913
 
0.9%
51423
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation154570
72.9%
Decimal Number57512
 
27.1%

Most frequent character per category

ValueCountFrequency (%)
026599
46.2%
18994
 
15.6%
63807
 
6.6%
93710
 
6.5%
43534
 
6.1%
83236
 
5.6%
72921
 
5.1%
21913
 
3.3%
51423
 
2.5%
31375
 
2.4%
ValueCountFrequency (%)
-154570
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common212082
100.0%

Most frequent character per script

ValueCountFrequency (%)
-154570
72.9%
026599
 
12.5%
18994
 
4.2%
63807
 
1.8%
93710
 
1.7%
43534
 
1.7%
83236
 
1.5%
72921
 
1.4%
21913
 
0.9%
51423
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII212082
100.0%

Most frequent character per block

ValueCountFrequency (%)
-154570
72.9%
026599
 
12.5%
18994
 
4.2%
63807
 
1.8%
93710
 
1.7%
43534
 
1.7%
83236
 
1.5%
72921
 
1.4%
21913
 
0.9%
51423
 
0.7%

tipo_de_garantia
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
REAL / PESSOAL
5575 
PESSOAL
4435 
DEFINIDA PELO AGENTE FINANCEIRO
4143 
REAL
1598 
NÃO SE APLICA
1272 
Other values (10)
2542 

Length

Max length91
Median length14
Mean length19.34674163
Min length4

Characters and Unicode

Total characters378519
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOUTRA, DE NATUREZA ESPECÍFICA OU MISTA
2nd rowNÃO SE APLICA
3rd rowNÃO SE APLICA
4th rowNÃO SE APLICA
5th rowDEFINIDA PELO AGENTE FINANCEIRO
ValueCountFrequency (%)
REAL / PESSOAL5575
28.5%
PESSOAL4435
22.7%
DEFINIDA PELO AGENTE FINANCEIRO4143
21.2%
REAL1598
 
8.2%
NÃO SE APLICA1272
 
6.5%
OUTRA, DE NATUREZA ESPECÍFICA OU MISTA1024
 
5.2%
COMPROMISSO DE DESEMPENHO FINANCEIRO ('COVENANTS')377
 
1.9%
PESSOAL / OUTRA, DE NATUREZA ESPECÍFICA OU MISTA335
 
1.7%
REAL / PESSOAL / OUTRA, DE NATUREZA ESPECÍFICA OU MISTA298
 
1.5%
REAL / OUTRA, DE NATUREZA ESPECÍFICA OU MISTA260
 
1.3%
Other values (5)248
 
1.3%
2021-08-18T23:56:22.233270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pessoal10643
17.4%
real7734
12.7%
6919
11.3%
financeiro4673
 
7.6%
definida4143
 
6.8%
pelo4143
 
6.8%
agente4143
 
6.8%
de2677
 
4.4%
natureza2070
 
3.4%
ou2070
 
3.4%
Other values (16)11877
19.4%

Most occurring characters

ValueCountFrequency (%)
E52075
13.8%
A45031
11.9%
41530
11.0%
S28972
 
7.7%
O27613
 
7.3%
L23792
 
6.3%
I23758
 
6.3%
N22666
 
6.0%
P19198
 
5.1%
R17348
 
4.6%
Other values (21)76536
20.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter325880
86.1%
Space Separator41530
 
11.0%
Other Punctuation10049
 
2.7%
Open Punctuation530
 
0.1%
Close Punctuation530
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
E52075
16.0%
A45031
13.8%
S28972
8.9%
O27613
8.5%
L23792
7.3%
I23758
7.3%
N22666
7.0%
P19198
 
5.9%
R17348
 
5.3%
D11518
 
3.5%
Other values (15)53909
16.5%
ValueCountFrequency (%)
/6919
68.9%
,2070
 
20.6%
'1060
 
10.5%
ValueCountFrequency (%)
41530
100.0%
ValueCountFrequency (%)
(530
100.0%
ValueCountFrequency (%)
)530
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin325880
86.1%
Common52639
 
13.9%

Most frequent character per script

ValueCountFrequency (%)
E52075
16.0%
A45031
13.8%
S28972
8.9%
O27613
8.5%
L23792
7.3%
I23758
7.3%
N22666
7.0%
P19198
 
5.9%
R17348
 
5.3%
D11518
 
3.5%
Other values (15)53909
16.5%
ValueCountFrequency (%)
41530
78.9%
/6919
 
13.1%
,2070
 
3.9%
'1060
 
2.0%
(530
 
1.0%
)530
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII375147
99.1%
None3372
 
0.9%

Most frequent character per block

ValueCountFrequency (%)
E52075
13.9%
A45031
12.0%
41530
11.1%
S28972
 
7.7%
O27613
 
7.4%
L23792
 
6.3%
I23758
 
6.3%
N22666
 
6.0%
P19198
 
5.1%
R17348
 
4.6%
Other values (17)73164
19.5%
ValueCountFrequency (%)
Í2070
61.4%
Ã1272
37.7%
Ê15
 
0.4%
Á15
 
0.4%

tipo_de_excepcionalidade
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
----------
19565 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters195650
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row----------
2nd row----------
3rd row----------
4th row----------
5th row----------
ValueCountFrequency (%)
----------19565
100.0%
2021-08-18T23:56:22.895152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:23.096083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
19565
100.0%

Most occurring characters

ValueCountFrequency (%)
-195650
100.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation195650
100.0%

Most frequent character per category

ValueCountFrequency (%)
-195650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common195650
100.0%

Most frequent character per script

ValueCountFrequency (%)
-195650
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII195650
100.0%

Most frequent character per block

ValueCountFrequency (%)
-195650
100.0%

situacao_do_contrato
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
ATIVO
9763 
LIQUIDADO
9592 
-
 
210

Length

Max length9
Median length5
Mean length6.91811909
Min length1

Characters and Unicode

Total characters135353
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLIQUIDADO
2nd rowLIQUIDADO
3rd rowLIQUIDADO
4th rowLIQUIDADO
5th rowLIQUIDADO
ValueCountFrequency (%)
ATIVO9763
49.9%
LIQUIDADO9592
49.0%
-210
 
1.1%
2021-08-18T23:56:23.579251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-08-18T23:56:23.741790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ativo9763
49.9%
liquidado9592
49.0%
210
 
1.1%

Most occurring characters

ValueCountFrequency (%)
I28947
21.4%
A19355
14.3%
O19355
14.3%
D19184
14.2%
T9763
 
7.2%
V9763
 
7.2%
L9592
 
7.1%
Q9592
 
7.1%
U9592
 
7.1%
-210
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter135143
99.8%
Dash Punctuation210
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
I28947
21.4%
A19355
14.3%
O19355
14.3%
D19184
14.2%
T9763
 
7.2%
V9763
 
7.2%
L9592
 
7.1%
Q9592
 
7.1%
U9592
 
7.1%
ValueCountFrequency (%)
-210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin135143
99.8%
Common210
 
0.2%

Most frequent character per script

ValueCountFrequency (%)
I28947
21.4%
A19355
14.3%
O19355
14.3%
D19184
14.2%
T9763
 
7.2%
V9763
 
7.2%
L9592
 
7.1%
Q9592
 
7.1%
U9592
 
7.1%
ValueCountFrequency (%)
-210
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII135353
100.0%

Most frequent character per block

ValueCountFrequency (%)
I28947
21.4%
A19355
14.3%
O19355
14.3%
D19184
14.2%
T9763
 
7.2%
V9763
 
7.2%
L9592
 
7.1%
Q9592
 
7.1%
U9592
 
7.1%
-210
 
0.2%

Interactions

2021-08-18T23:55:34.980128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:35.269704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:35.578416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:35.835407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:36.092481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:36.393887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:36.791274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:37.203216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:38.363368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:38.660123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:38.946006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:39.221243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:39.470378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:39.735485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:39.979608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:40.278396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:40.520350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:40.784054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:41.025086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:41.282490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:41.552897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:41.785471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:42.041639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:42.317783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:42.564261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:42.794694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:43.035830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:43.283122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:43.526575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:43.753803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:43.993782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:44.229486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:44.465270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:44.713108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:44.938718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:45.184160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:45.435208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:45.678581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:45.983871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:46.362747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:46.670848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-18T23:55:47.091687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-18T23:56:23.924415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-18T23:56:24.302133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-18T23:56:24.616828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-18T23:56:24.986141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-18T23:56:25.762805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-18T23:55:47.863573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-18T23:55:51.026887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

clientecnpjdescricao_do_projetoufmunicipiomunicipio_codigonumero_do_contratodata_da_contratacaovalor_contratado_reaisvalor_desembolsado_reaisfonte_de_recurso_desembolsoscusto_financeirojurosprazo_carencia_mesesprazo_amortizacao_mesesmodalidade_de_apoioforma_de_apoioprodutoinstrumento_financeiroinovacaoarea_operacionalsetor_cnaesubsetor_cnae_agrupadosubsetor_cnae_codigosubsetor_cnae_nomesetor_bndessubsetor_bndesporte_do_clientenatureza_do_clienteinstituicao_financeira_credenciadacnpj_da_instituicao_financeira_credenciadatipo_de_garantiatipo_de_excepcionalidadesituacao_do_contrato
0MUNICIPIO DE RIBEIRAO PRETO56.024.581/0001-56PROGRAMA DE MODERNIZACAO DA ADMINISTRACAO TRIBUTARIA E DA GESTAO DOS SETORES SOCIAIS BASICOSSPRIBEIRAO PRETO354340212470212002-01-029090000.009007445.10RECURSOS VINCULADOS - PIS/PASEPTJLP2.52472REEMBOLSÁVELDIRETABNDES FINEMBNDES PMATNÃOAREA DE GESTAO PUBLICA E SOCIOAMBIENTALCOMERCIO E SERVICOSADMINISTRAÇÃO PÚBLICAO8411600ADMINISTRACAO PUBLICA EM GERALCOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSGRANDEADMINISTRAÇÃO PÚBLICA DIRETA - GOVERNO MUNICIPAL--------------------OUTRA, DE NATUREZA ESPECÍFICA OU MISTA----------LIQUIDADO
1INSTITUTO DE DESENVOLVIMENTO SUSTENTAVEL DO BAIXO SUL D02.275.306/0001-86PROJETO DIREITO E CIDADANIA; PROJETO DEMONSTRATIVO DA CADEIA PRODUTIVA DE MARICULTURA; E SISTEMATIZACAO DA METODOLOGIA DO PROGRAMA JOVEM EMPRESARIO.BAITUBERA291730012472212002-01-03706600.00745030.36RECURSOS ESTATUTÁRIOS - PRÓPRIOS ESTATUTÁRIOSSEM CUSTO0.000NÃO REEMBOLSÁVELDIRETABNDES NÃO REEMBOLSÁVELFUNDO SOCIALNÃOAREA DE GESTAO PUBLICA E SOCIOAMBIENTALCOMERCIO E SERVICOSSAÚDE E SERV SOCIALQ8700000ATIV ATENCAO A SAUDE HUMANA INTEGR C/ASSIST SOC PREST RESID COL ECOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSMICROPRIVADA--------------------NÃO SE APLICA----------LIQUIDADO
2INSTITUTO DE DESENVOLVIMENTO SUSTENTAVEL DO BAIXO SUL D02.275.306/0001-86PROJETO DIREITO E CIDADANIA; PROJETO DEMONSTRATIVO DA CADEIA PRODUTIVA DE MARICULTURA; E SISTEMATIZACAO DA METODOLOGIA DO PROGRAMA JOVEM EMPRESARIO.BAITUBERA291730012472212002-01-0375691.5978500.00RECURSOS ESTATUTÁRIOS - PRÓPRIOS ESTATUTÁRIOSSEM CUSTO0.000NÃO REEMBOLSÁVELDIRETABNDES NÃO REEMBOLSÁVELFUNDO SOCIALNÃOAREA DE GESTAO PUBLICA E SOCIOAMBIENTALCOMERCIO E SERVICOSSAÚDE E SERV SOCIALQ8700000ATIV ATENCAO A SAUDE HUMANA INTEGR C/ASSIST SOC PREST RESID COL ECOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSMICROPRIVADA--------------------NÃO SE APLICA----------LIQUIDADO
3INSTITUTO DE DESENVOLVIMENTO SUSTENTAVEL DO BAIXO SUL D02.275.306/0001-86PROJETO DIREITO E CIDADANIA; PROJETO DEMONSTRATIVO DA CADEIA PRODUTIVA DE MARICULTURA; E SISTEMATIZACAO DA METODOLOGIA DO PROGRAMA JOVEM EMPRESARIO.BAITUBERA291730012472212002-01-03603981.41653834.69RECURSOS ESTATUTÁRIOS - PRÓPRIOS ESTATUTÁRIOSSEM CUSTO0.000NÃO REEMBOLSÁVELDIRETABNDES NÃO REEMBOLSÁVELFUNDO SOCIALNÃOAREA DE GESTAO PUBLICA E SOCIOAMBIENTALCOMERCIO E SERVICOSSAÚDE E SERV SOCIALQ8700000ATIV ATENCAO A SAUDE HUMANA INTEGR C/ASSIST SOC PREST RESID COL ECOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSMICROPRIVADA--------------------NÃO SE APLICA----------LIQUIDADO
4ACEF S/A46.722.831/0001-78AQUISICAO DE EQUIPAMENTOS NACIONAIS E MOBILIARIO.SPFRANCA351620012554212002-01-09340201.00340189.32RECURSOS LIVRES - FATTJLP4.02496REEMBOLSÁVELINDIRETABNDES FINEMPROGRAMA IESNÃOAREA DE GESTAO PUBLICA E SOCIOAMBIENTALCOMERCIO E SERVICOSEDUCAÇÃOP8531700EDUCACAO SUPERIOR - GRADUACAOCOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSGRANDEPRIVADABANCO BRADESCO S.A.60746948000112DEFINIDA PELO AGENTE FINANCEIRO----------LIQUIDADO
5ACEF S/A46.722.831/0001-78AQUISICAO DE EQUIPAMENTOS NACIONAIS E MOBILIARIO.SPFRANCA351620012554212002-01-093423449.003077791.61RECURSOS LIVRES - FATTJLP4.02496REEMBOLSÁVELINDIRETABNDES FINEMPROGRAMA IESNÃOAREA DE GESTAO PUBLICA E SOCIOAMBIENTALCOMERCIO E SERVICOSEDUCAÇÃOP8531700EDUCACAO SUPERIOR - GRADUACAOCOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSGRANDEPRIVADABANCO BRADESCO S.A.60746948000112DEFINIDA PELO AGENTE FINANCEIRO----------LIQUIDADO
6VIDEOLAR-INNOVA S/A04.229.761/0001-70IMPLANTACAO DE UNIDADE PRODUTORA DE POLIESTIRENO CRISTAL, EMMANAUS/AM.AMMANAUS130260312351512002-01-10500000.00533962.41RECURSOS LIVRES - FAT / RECURSOS LIVRES - PRÓPRIOSTJLP1.02448REEMBOLSÁVELDIRETABNDES FINEMOUTROSNÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOEQUIP INFO, ELETRONICO, ÓTICOC2630000FABRICACAO DE EQUIPAMENTOS DE COMUNICACAOINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------REAL / PESSOAL----------LIQUIDADO
7VIDEOLAR-INNOVA S/A04.229.761/0001-70IMPLANTACAO DE UNIDADE PRODUTORA DE POLIESTIRENO CRISTAL, EMMANAUS/AM.AMMANAUS130260312351512002-01-105714000.005340895.61RECURSOS LIVRES - FAT / RECURSOS LIVRES - PRÓPRIOSUS$ / CESTA4.51260REEMBOLSÁVELDIRETABNDES FINEMOUTROSNÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOEQUIP INFO, ELETRONICO, ÓTICOC2630000FABRICACAO DE EQUIPAMENTOS DE COMUNICACAOINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------REAL / PESSOAL----------LIQUIDADO
8VIDEOLAR-INNOVA S/A04.229.761/0001-70IMPLANTACAO DE UNIDADE PRODUTORA DE POLIESTIRENO CRISTAL, EMMANAUS/AM.AMMANAUS130260312351512002-01-1029680000.8429994060.17RECURSOS LIVRES - FAT / RECURSOS LIVRES - PRÓPRIOSTJLP4.51260REEMBOLSÁVELDIRETABNDES FINEMOUTROSNÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOEQUIP INFO, ELETRONICO, ÓTICOC2630000FABRICACAO DE EQUIPAMENTOS DE COMUNICACAOINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------REAL / PESSOAL----------LIQUIDADO
9USINA CAETE S A12.282.034/0001-03EXPANSAO E MODERNIZACAO DE CINCO CENTRAIS TERMELETRICAS A BAGACO DE CANA COM 75 MW DE POTENCIA INSTALADA.ALMACEIO270430212522312002-01-116323444.036408000.00RECURSOS LIVRES - PRÓPRIOS / RECURSOS VINCULADOS - FAT DEPÓSITOS ESPECIAISTJLP3.530120REEMBOLSÁVELDIRETABNDES FINEMOUTROSNÃOAREA DE INDUSTRIA E SERVICOSCOMERCIO E SERVICOSELETRICIDADE E GÁSD3511584GERACAO DE ENERGIA ELETRICA - CO-GERACAO CANA-DE-ACUINFRAESTRUTURAENERGIA ELÉTRICAGRANDEPRIVADA--------------------REAL / PESSOAL----------LIQUIDADO

Last rows

clientecnpjdescricao_do_projetoufmunicipiomunicipio_codigonumero_do_contratodata_da_contratacaovalor_contratado_reaisvalor_desembolsado_reaisfonte_de_recurso_desembolsoscusto_financeirojurosprazo_carencia_mesesprazo_amortizacao_mesesmodalidade_de_apoioforma_de_apoioprodutoinstrumento_financeiroinovacaoarea_operacionalsetor_cnaesubsetor_cnae_agrupadosubsetor_cnae_codigosubsetor_cnae_nomesetor_bndessubsetor_bndesporte_do_clientenatureza_do_clienteinstituicao_financeira_credenciadacnpj_da_instituicao_financeira_credenciadatipo_de_garantiatipo_de_excepcionalidadesituacao_do_contrato
19555AGROPECUARIA SCHIO LTDA91.501.783/0001-42CONTRATACAO DE CREDITO PARA AQUISICAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS.RSVACARIA4322509195000612021-05-261.912704e+050.0RECURSOS LIVRES - FATTLP5.3036156REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSAGROPECUÁRIA E PESCAAGROPECUÁRIAA0133407CULTIVO DE MACAAGROPECUÁRIAAGROPECUÁRIAGRANDEPRIVADA--------------------REAL / PESSOAL----------ATIVO
19556SCHULZ COMPRESSORES LTDA23.635.798/0001-43CONTRATACAO DE LIMITE DE CREDITO PARA FINANCIAMENTO A AQUISICAO DE MAQUINAS, EQUIPAMENTOS, MATERIAIS INDUSTRIALIZADOS E/OU DE CAPITAL DE GIRO ASSOCIADO, BEM COMO COMERCIALIZACAO OUPRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999208003412021-05-277.475000e+050.0-SELIC1.6936156REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃOSIMAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQUINAS E EQUIPAMENTOSC2814301FABRICACAO DE COMPRESSORES PARA USO INDUSTRIAL, PECA E ACESSORIOINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------REAL----------ATIVO
19557SCHULZ COMPRESSORES LTDA23.635.798/0001-43CONTRATACAO DE LIMITE DE CREDITO PARA FINANCIAMENTO A AQUISICAO DE MAQUINAS, EQUIPAMENTOS, MATERIAIS INDUSTRIALIZADOS E/OU DE CAPITAL DE GIRO ASSOCIADO, BEM COMO COMERCIALIZACAO OUPRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999208003412021-05-277.617001e+050.0-SELIC2.0936156REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQUINAS E EQUIPAMENTOSC2814301FABRICACAO DE COMPRESSORES PARA USO INDUSTRIAL, PECA E ACESSORIOINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------REAL----------ATIVO
19558SCHULZ S/A84.693.183/0001-68CONTRATACAO DE LIMITE DE CREDITO PARA FINANCIAMENTO A AQUISICAO DE MAQUINAS, EQUIPAMENTOS, MATERIAIS INDUSTRIALIZADOS E/OU DE CAPITAL DE GIRO ASSOCIADO, BEM COMO COMERCIALIZACAO OUPRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999208003412021-05-274.181970e+060.0-SELIC2.0936156REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQUINAS E EQUIPAMENTOSC2812700FAB EQ HIDRAULICO PNEUMATICO PECA E ACESSORIO EXCETO VALVULASINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------REAL----------ATIVO
19559MUELLER FOGOES LTDA.04.565.361/0001-36CONTRATACAO DE CREDITO PARA AQUISICAO, COMERCIALIZACAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999194000112021-05-274.480000e+060.0-TLP1.393684REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQ, APARELHO ELETRICOC2751100FAB FOGAO REFRIGERA MAQ LAVAR SECAR P/USO DOMESTICO PECA ACESSINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------PESSOAL----------ATIVO
19560HERCULES MOTORES ELETRICOS LTDA07.442.711/0001-65CONTRATACAO DE CREDITO PARA AQUISICAO, COMERCIALIZACAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999194000112021-05-277.360000e+050.0-TLP1.393684REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQ, APARELHO ELETRICOC2710403FABRICACAO DE MOTORES ELETRICOS, PECAS E ACESSORIOSINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------PESSOAL----------ATIVO
19561MUELLER FOGOES LTDA.04.565.361/0001-36CONTRATACAO DE CREDITO PARA AQUISICAO, COMERCIALIZACAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999194000112021-05-278.800000e+040.0-TLP1.393684REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQ, APARELHO ELETRICOC2751100FAB FOGAO REFRIGERA MAQ LAVAR SECAR P/USO DOMESTICO PECA ACESSINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------PESSOAL----------ATIVO
19562MUELLER FOGOES LTDA.04.565.361/0001-36CONTRATACAO DE CREDITO PARA AQUISICAO, COMERCIALIZACAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999194000112021-05-271.045640e+050.0-TLP1.393684REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQ, APARELHO ELETRICOC2751100FAB FOGAO REFRIGERA MAQ LAVAR SECAR P/USO DOMESTICO PECA ACESSINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------PESSOAL----------ATIVO
19563MUELLER FOGOES LTDA.04.565.361/0001-36CONTRATACAO DE CREDITO PARA AQUISICAO, COMERCIALIZACAO OU PRODUCAO DE MAQUINAS E EQUIPAMENTOS.SCSEM MUNICÍPIO9999999194000112021-05-281.802855e+060.0-TLP1.393684REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE INDUSTRIA E SERVICOSINDUSTRIA DE TRANSFORMAÇÃOMÁQ, APARELHO ELETRICOC2751100FAB FOGAO REFRIGERA MAQ LAVAR SECAR P/USO DOMESTICO PECA ACESSINDUSTRIAMECÂNICAGRANDEPRIVADA--------------------PESSOAL----------ATIVO
19564CONCESSIONARIA DE RODOVIAS PIRACICABA PANORAMA S.A.36.146.575/0001-64APOIO, VIA DEBENTURES, AOS INVESTIMENTOS PREVISTOS NO PLANO ORIGINAL DE INVESTIMENTOS DO CONTRATO ARTESP NO 0409/ARTESP/2020, FIRMADO ENTRE A ARTESP E A EIXO SP, REFERENTE AO SISTEMA RODOVIARIO DENOMINADO LOTE PIRACICABA-PANORAMA PIPA.SPDIVERSOS9999999212015812021-05-313.500000e+080.0-TLP5.0542132REEMBOLSÁVELDIRETABNDES DEBENTURES SUSTENTAVEIS E DE INFRAESTRUTURABNDES DEBÊNTURES SUSTENTÁVEIS E DE INFRAESTRUTURANÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSATIV AUX TRANSPORTE E ENTREGAH5221400CONCESSIONARIAS RODOVIAS, PONTES, TUNEIS E SERVICOS RELACIONADOSINFRAESTRUTURAATV. AUX. TRANSPORTESGRANDEPRIVADA--------------------REAL / PESSOAL-----------

Duplicate rows

Most frequent

clientecnpjdescricao_do_projetoufmunicipiomunicipio_codigonumero_do_contratodata_da_contratacaovalor_contratado_reaisvalor_desembolsado_reaisfonte_de_recurso_desembolsoscusto_financeirojurosprazo_carencia_mesesprazo_amortizacao_mesesmodalidade_de_apoioforma_de_apoioprodutoinstrumento_financeiroinovacaoarea_operacionalsetor_cnaesubsetor_cnae_agrupadosubsetor_cnae_codigosubsetor_cnae_nomesetor_bndessubsetor_bndesporte_do_clientenatureza_do_clienteinstituicao_financeira_credenciadacnpj_da_instituicao_financeira_credenciadatipo_de_garantiatipo_de_excepcionalidadesituacao_do_contratocount
14AUTOPISTA REGIS BITTENCOURT S/A09.336.431/0001-06APOIO FINANCEIRO AOS INVESTIMENTOS PREVISTOS NO 2 CICLOIESEM MUNICÍPIO0172033712017-09-011.419200e+070.000000e+00RECURSOS LIVRES - FAT / RECURSOS LIVRES - TESOUROTJLP3.7433114REEMBOLSÁVELDIRETABNDES PROJECT FINANCELOGÍSTICA - Modal RodoviárioNÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSATIV AUX TRANSPORTE E ENTREGAH5221400CONCESSIONARIAS RODOVIAS, PONTES, TUNEIS E SERVICOS RELACIONADOSINFRAESTRUTURAATV. AUX. TRANSPORTESGRANDEPRIVADA--------------------REAL----------ATIVO6
15AUTOPISTA REGIS BITTENCOURT S/A09.336.431/0001-06SUPLEMENTACAO DE RECURSOS PARA O CONTRATO DE FINANCIAMENTO REFERENTE AO 1 CICLO DE INVESTIMENTOS DA CONCESSAO DO EDITAL ANTT 01/2007 LOTE 6IESEM MUNICÍPIO0172033712017-09-014.051600e+070.000000e+00RECURSOS LIVRES - FAT / RECURSOS LIVRES - TESOUROTJLP3.7433114REEMBOLSÁVELDIRETABNDES PROJECT FINANCELOGÍSTICA - Modal RodoviárioNÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSCONSTRUÇÃOF4211101CONSTRUCAO DE RODOVIAS E FERROVIASINFRAESTRUTURACONSTRUÇÃOGRANDEPRIVADA--------------------REAL----------ATIVO6
112ITAETE MOVIMENTACAO - LOGISTICA LTDA05.685.282/0001-21CONTRATACAO DE CREDITO PARA AQUISICAO DE MAQUINAS E EQUIPAMENTOS.PRCURITIBA4106902208000512021-01-115.580000e+050.000000e+00RECURSOS LIVRES - FATTLP4.212448REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSATIV AUX TRANSPORTE E ENTREGAH5250804ORGANIZACAO LOGISTICA DO TRANSPORTE DE CARGAINFRAESTRUTURAATV. AUX. TRANSPORTESMÉDIAPRIVADA--------------------REAL----------ATIVO6
122MC LOG S.A. LOGISTICA E TRANSPORTE07.521.328/0001-00CONSTRUCAO DE DEZOITO BALSAS GRANELEIRAS, TRES EMPURRADORESE UMA BALSA COM GUINDASTE PARA TRANSPORTE DE FERRO GUSA E MINERIOS DA REGIAO DE CARAJAS.IMPLANTACAO DE UMA ESCOLA PARA FLUVIARIOS, EM MARABA-PA.PABELEM150140282015712008-11-261.710785e+055.671429e+04RECURSOS LIVRES - PRÓPRIOSUS$ / CESTA4.0718240REEMBOLSÁVELDIRETABNDES FINEMFUNDO DA MARINHA MERCANTENÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSTRANSPORTE AQUAVIÁRIOH5021100TRANSPORTE POR NAVEGACAO INTERIOR DE CARGAINFRAESTRUTURAOUTROS TRANSPORTESGRANDEPRIVADA--------------------REAL / PESSOAL----------ATIVO6
4AIR CATERING FORNECIMENTO DE ALIMENTOS LTDA.07.182.194/0001-32INVESTIMENTOS RECORRENTES E CAPITAL DE GIRO.IESEM MUNICÍPIO9999999192057912019-11-088.000000e+050.000000e+00-TLP2.351236REEMBOLSÁVELDIRETABNDES CRÉDITO DIRETO MÉDIAS EMPRESASBNDES CRÉDITO DIRETO MÉDIAS EMPRESAS - GIRONÃOAREA DE INDUSTRIA E SERVICOSCOMERCIO E SERVICOSALOJAMENTO E ALIMENTAÇÃOI5620101FORNECIMENTO ALIMENTO PREPARADO PREPONDERANTEMENTE PARA EMPRESASCOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSMÉDIAPRIVADA--------------------PESSOAL----------ATIVO5
5AIR CATERING FORNECIMENTO DE ALIMENTOS LTDA.07.182.194/0001-32INVESTIMENTOS RECORRENTES E CAPITAL DE GIRO.IESEM MUNICÍPIO9999999192057912019-11-081.200000e+060.000000e+00-TLP1.9518102REEMBOLSÁVELDIRETABNDES CRÉDITO DIRETO MÉDIAS EMPRESASBNDES CRÉDITO DIRETO MÉDIAS EMPRESAS - INVESTIMENTONÃOAREA DE INDUSTRIA E SERVICOSCOMERCIO E SERVICOSALOJAMENTO E ALIMENTAÇÃOI5620101FORNECIMENTO ALIMENTO PREPARADO PREPONDERANTEMENTE PARA EMPRESASCOMERCIO/SERVICOSCOMÉRCIO E SERVIÇOSMÉDIAPRIVADA--------------------PESSOAL----------ATIVO5
111ITAETE MOVIMENTACAO - LOGISTICA LTDA05.685.282/0001-21CONTRATACAO DE CREDITO PARA AQUISICAO DE MAQUINAS E EQUIPAMENTOS.PRCURITIBA4106902208000512020-12-095.580000e+055.580000e+05RECURSOS LIVRES - FATTLP4.212448REEMBOLSÁVELDIRETABNDES FINAMEBK AQUISIÇÃO E COMERCIALIZAÇÃONÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSATIV AUX TRANSPORTE E ENTREGAH5250804ORGANIZACAO LOGISTICA DO TRANSPORTE DE CARGAINFRAESTRUTURAATV. AUX. TRANSPORTESMÉDIAPRIVADA--------------------REAL----------ATIVO5
68CTRENS- COMPANHIA DE MANUTENCAO11.656.505/0001-25AQUISICAO DE NO MINIMO 32, PODENDO ALCANCAR ATE 36 TRENS NOVOS DE 8 CARROS CADA, PARA A LINHA 8 - DIAMANTE DA COMPANHIA PAULISTA DE TRENS METROPOLITANOS - CPTMSPSAO PAULO3550308112007012011-05-101.578150e+081.578150e+08RECURSOS LIVRES - FATTJLP2.0519160REEMBOLSÁVELDIRETABNDES PROJECT FINANCEPROJETOS ESTRUTURADORES DE TRANSPORTE URBANONÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSTRANSPORTE TERRESTREH4912400TRANSPORTE METROFERROVIARIO DE PASSAGEIROSINFRAESTRUTURAOUTROS TRANSPORTESGRANDEPRIVADA--------------------OUTRA, DE NATUREZA ESPECÍFICA OU MISTA----------ATIVO4
99HERMASA NAVEGACAO DA AMAZONIA S/A84.590.892/0001-18CONSTRUCAO DE 2 EMPURRADORES FLUVIAIS DE 4400 BHP; 18 BALSASGRANELEIRAS DE 2000 TPB; 18 BALSAS GRANELEIRAS DE 1850 TPB;E JUMBORIZACAO DO TERMINAL FLUTUANTE DE ITACOATIARA. ESTALEIRO: ERINAMMANAUS130260342405312004-08-161.759724e+061.367060e+06RECURSOS VINCULADOS - TESOUROUS$ / CESTA5.0018180REEMBOLSÁVELDIRETABNDES FINEMFUNDO DA MARINHA MERCANTENÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSTRANSPORTE AQUAVIÁRIOH5021100TRANSPORTE POR NAVEGACAO INTERIOR DE CARGAINFRAESTRUTURAOUTROS TRANSPORTESGRANDEPRIVADA--------------------REAL / PESSOAL----------LIQUIDADO4
100HERMASA NAVEGACAO DA AMAZONIA S/A84.590.892/0001-18CONSTRUCAO DE 2 EMPURRADORES FLUVIAIS DE 4400 BHP; 18 BALSASGRANELEIRAS DE 2000 TPB; 18 BALSAS GRANELEIRAS DE 1850 TPB;E JUMBORIZACAO DO TERMINAL FLUTUANTE DE ITACOATIARA. ESTALEIRO: ERINAMMANAUS130260342405312004-08-161.759724e+061.506515e+06RECURSOS VINCULADOS - TESOUROUS$ / CESTA5.0018180REEMBOLSÁVELDIRETABNDES FINEMFUNDO DA MARINHA MERCANTENÃOAREA DE SANEAMENTO E TRANSPORTECOMERCIO E SERVICOSTRANSPORTE AQUAVIÁRIOH5021100TRANSPORTE POR NAVEGACAO INTERIOR DE CARGAINFRAESTRUTURAOUTROS TRANSPORTESGRANDEPRIVADA--------------------REAL / PESSOAL----------LIQUIDADO4